Semantics and aesthetics inference for image search: statistical learning approaches
Inhaltsverzeichnis
Reference
Datta, Ritendra: Semantics and aesthetics inference for image search: statistical learning approaches. Dissertation at Pennsylvania State University (2009)
DOI
Abstract
Extended Abstract
The automatic inference of image semantics is an important but highly challenging research problem whose solutions can greatly benefit content-based image search and automatic image annotation. In this thesis, I present algorithms and statistical models for inferring image semantics and aesthetics from visual content, specifically aimed at improving real-world image search. First, a novel approach to automatic image tagging is presented which furthers the state-of-the-art in both speed and accuracy. The direct use of automatically generated tags in real-world image search is then explored, and its efficacy demonstrated experimentally. An assumption which makes most annotation models misrepresent reality is that the state of the world is static, whereas it is fundamentally dynamic. I explore learning algorithms for adapting automatic tagging to different scenario changes. Specifically, a meta-learning model is proposed which can augment a black-box annotation model to help provide adaptability for personalization, time evolution, and contextual changes. Instead of retraining expensive annotation models, adaptability is achieved through efficient incremental learning of only the meta-learning component. Large scale experiments convincingly support this approach. In image search, when semantics alone yields many matches, one way to rank images further is to look beyond semantics and consider visual quality. I explore the topic of data-driven inference of aesthetic quality of images. Owing to minimal prior art, the topic is first explored in detail. Then, methods for extracting a number of high-level visual features, presumed to have correlation with aesthetics, are presented. Through feature selection and machine learning, an aesthetics inference model is trained and found to perform moderately on real-world data. The aesthetics-correlated visual features are then used in the problem of selecting and eliminating images at the high and low extremes of the aesthetics scale respectively, using a novel statistical model. Experimentally, this approach is found to work well in visual quality based filtering. Finally, I explore the use of image search techniques for designing a novel image-based CAPTCHA, a Web security test aimed at distinguishing humans from machines. Assuming image search metrics to be potential attack tools, they are used in the loop to design attack-resistant CAPTCHAs.
Bibtex
Used References
[1] P. Aigrain, H. Zhang, and D. Petkovic. Content-based representation and retrieval of visual media: A review of the state-of-the-art. Multimedia Tools and Applications, 3(3):179–202, 1996.
[2] Airliners.net, 2006. http://www.airliners.net.
[3] Alipr, 2006. http://www.alipr.com.
[4] J. Amores, N. Sebe, and P. Radeva. Fast spatial pattern discovery integrating boosting with constellations of contextual descriptors. In Proc. IEEE CVPR, 2005.
[5] J. Amores, N. Sebe, and P. Radeva. Boosting the distance estimation: Application to the k-nearest neighbor classifier. Pattern Recognition Letters, 27(3):201–209, 2006.
[6] J. Amores, N. Sebe, P. Radeva, T. Gevers, and A. Smeulders. Boosting contextual information in content-based image retrieval. In Proc. MIR Workshop, ACM Multimedia, 2004.
[7] ARTStor.org, 2006. http://www.artstor.org.
[8] A. Bar-hillel, T. Hertz, N. Shental, and D.Weinshall. Learning a mahalanobis metric from equivalence constraints. J. Machine Learning Research, 6:937– 965, 2005.
[9] K. Barnard, P. Duygulu, D. Forsyth, N. deFreitas, D.M. Blei, and M.I. Jordan. Matching words and pictures. J. Machine Learning Research, 3:1107– 1135, 2003.
[10] I. Bartolini, P. Ciaccia, and M. Patella. Warp: Accurate retrieval of shapes using phase of fourier descriptors and time warping distance. IEEE Trans. Pattern Analysis and Machine Intelligence, 27(1):142–147, 2005.
[11] S. Belongie, J. Malik, and J. Puzicha. Shape matching and object recognition using shape contexts. IEEE Trans. Pattern Analysis and Machine Intelligence, 24(4):509–522, 2002.
[12] S. Berretti and A. Del Bimbo. Modeling spatial relationships between 3d objects. In Proc. IEEE ICPR, 2006.
[13] S. Berretti, A. Del Bimbo, and P. Pala. Retrieval by shape similarity with perceptual distance and effective indexing. IEEE Trans. Multimedia, 2(4):225–239, 2000.
[14] S. Berretti, A. Del Bimbo, and E. Vicario. Weighted walkthroughs between extended entities for retrieval by spatial arrangement. IEEE Trans. Multimedia, 5(1):52–70, 2003.
[15] D. M. Blei and M. I. Jordan. Modeling annotated data. In Proc. ACM SIGIR, 2003.
[16] D.M. Blei, A.Y. Ng, and M.I. Jordan. Latent dirichlet allocation. J. Machine Learning Research, 3:993–1022, 2003.
[17] A.L. Blum and P. Langley. Selection of relevant features and examples in machine learning. Artificial Intelligence, 97(1-2):245–271, 1997.
[18] A.L. Blum and P. Langley. Selection of relevant features and examples in machine learning. Artificial Intelligence, 97(1–2):245–271, 1997.
[19] C. Bohm, S. Berchtold, and D. A. Keim. Searching in high-dimensional space index structures for improving the performance of multimedia databases. ACM Computing Surveys, 33(3):322–373, 2001.
[20] G. Bouchard and B. Triggs. Hierarchical part-based visual object categorization. In Proc. IEEE CVPR, 2005.
[21] C.A. Bouman. Cluster: An unsupervised algorithm for modeling gaussian mixtures, 2006. http://www.ece.purdue.edu/~bouman/.
[22] L. Breiman, J.H. Friedman, R.A. Olshen, and C.J. Stone. Classification and Regression Trees. Wadsworth, Belmont, 1983.
[23] C. c. Chang and C. j. Lin. Libsvm : A library for SVM, 2006. http: //www.csie.ntu.edu.tw/~cjlin/libsvm/.
[24] D. Cai, X. He, Z. Li, W. Y. Ma, and J. R. Wen. Hierarchical clustering of www image search results using visual, textual and link information. In Proc. ACM Multimedia, 2004.
[25] Captcha.net, 2006. http://www.captcha.net.
[26] J. Carballido-Gamio, S. Belongie, and S. Majumdar. Normalized cuts in 3-d for spinal MRI segmentation. IEEE Trans. Medical Imaging, 23(1):36–44, 2004.
[27] G. Carneiro, A.B. Chan, P.J. Moreno, and N. Vasconcelos. Supervised learning of semantic classes for image annotation and retrieval. IEEE Trans. Pattern Analysis and Machine Intelligence, 29(3):394–410, 2007.
[28] G. Carneiro and D. Lowe. Sparse flexible models of local features. In Proc. ECCV, 2006.
[29] G. Carneiro and N. Vasconcelos. Minimum bayes error features for visual recognition by sequential feature selection and extraction. In Proc. Canadian Conf. Computer and Robot Vision, 2005.
[30] C. Carson, S. Belongie, H. Greenspan, and J. Malik. Blobworld: Color and texture-based image segmentation using em and its application to image querying and classification. IEEE Trans. Pattern Analysis and Machine Intelligence, 24(8):1026–1038, 2002.
[31] C. Carson, S. Belongie, H. Greenspan, and J. Malik. Blobworld: Image segmentation using expectation-maximization and its application to image querying. IEEE Trans. Pattern Analysis and Machine Intelligence, 24(8):1026–1038, 2002.
[32] R. Caruana. Multitask learning. Machine Learning, 28(1):41–75, 1997.
[33] G. Cauwenberghs and T. Poggio. Incremental and decremental support vector machine learning. In Proc. NIPS, 2001.
[34] E. Y. Chang, K. Goh, G. Sychay, and G. Wu. CBSA: Content-based soft annotation for multimodal image retrieval using bayes point machines. IEEE Trans. Circuits and Systems for Video Technology, 13(1):26–38, 2003.
[35] S.-F. Chang, J.R. Smith, M. Beigi, and A. Benitez. Visual information retrieval from large distributed online repositories. Communications of the ACM, 40(12):63–71, 1997.
[36] S.K. Chang, Q.Y. Shi, and C.W. Yan. Iconic indexing by 2-d strings. IEEE Trans. Pattern Analysis and Machine Intelligence, 9(3):413–427, 1987.
[37] S.K. Chang, C.W. Yan, D.C. Dimitroff, and T. Arndt. An intelligent image database system. IEEE Trans. Software Engineering, 14(5):681–688, 1988.
[38] K. Chellapilla and P.Y. Simard. Using machine learning to break visual human interaction proofs (HIPs). In Proc. NIPS, 2004.
[39] C.-C. Chen, H. Wactlar, J. Z.Wang, and K. Kiernan. Digital imagery for significant cultural and historical materials - an emerging research field bridging people, culture, and technologies. Intl. J. on Digital Libraries, 5(4):275–286, 2005.
[40] J. Chen, T.N. Pappas, A. Mojsilovic, and B. Rogowitz. Adaptive image segmentation based on color and texture. In Proc. IEEE ICIP, 2002.
[41] Y. Chen and J. Z. Wang. A region-based fuzzy feature matching approach to content-based image retrieval. IEEE Trans. Pattern Analysis and Machine Intelligence, 24(9):252–1267, 2002.
[42] Y. Chen and J. Z. Wang. Image categorization by learning and reasoning with regions. J. Machine Learning Research, 5:913–939, 2004.
[43] Y. Chen, J. Z. Wang, and R. Krovetz. CLUE: Cluster-based retrieval of images by unsupervised learning. IEEE Trans. Image Processing, 14(8):1187– 1201, 2005.
[44] Y. Chen, X. Zhou, and T. S. Huang. One-class SVM for learning in image retrieval. In Proc. IEEE ICIP, 2002.
[45] M. Chew and J. D. Tygar. Image recognition CAPTCHAs. In Proc. Information Security Conf., 2004.
[46] M. Chew and J.D. Tygar. Image recognition CAPTCHAs. In Proc. ISC, 2004.
[47] P. Ciaccia, M. Patella, and P. Zezula. M-tree: An efficient access method for similarity search in metric spaces. In Proc. VLDB, 1997. [48] CNN. Computer decodes mona lisa’s smile. CNN - Technology, 12/16/2005, 2005.
[49] D. Comaniciu and P. Meer. Mean shift: A robust approach toward feature space analysis. IEEE Trans. Pattern Analysis and Machine Intelligence, 24(5):603–619, 2002.
[50] I. J. Cox, M. L. Miller, T. P. Minka, T. V. Papathomas, and P. N. Yianilos. The bayesian image retrieval system, pichunter: Theory, implementation, and psychophysical experiments. IEEE Trans. Image Processing, 9(1):20– 37, 2000.
[51] I.J. Cox, J. Kilian, F.T. Leighton, and T. Shamoon. Secure spread spectrum watermarking for multimedia. IEEE Trans. Image Processing, 6(12):1673– 1687, 1997.
[52] A. Csillaghy, H. Hinterberger, and A.O. Benz. Content based image retrieval in astronomy. Information Retrieval, 3(3):229–241, 2000.
[53] I. Dagan, L. Lee, and F.C.N. Pereira. Similarity-based models of word cooccurrence probabilities. Machine Learning, 34(1-3):43–69, 1999.
[54] C. Dagli and T. S. Huang. A framework for grid-based image retrieval. In Proc. IEEE ICPR, 2004.
[55] R. Datta, W. Ge, J. Li, and J.Z. Wang. Toward bridging the annotationretrieval gap in image search. IEEE MultiMedia, 14(3):24–35, 2007.
[56] R. Datta, D. Joshi, J. Li, and J. Z. Wang. Studying aesthetics in photographic images using a computational approach. In Proc. ECCV, 2006.
[57] R. Datta, D. Joshi, J. Li, and J.Z. Wang. Tagging over time: Real-world image annotation by lightweight meta-learning. In Proc. ACM Multimedia, 2007. [58] R. Datta, D. Joshi, J. Li, and J.Z. Wang. Image retrieval: Ideas, influences, and trends of the new age. ACM Computing Surveys, 40(2):1–60, 2008.
[59] R. Datta, J. Li, and J. Z. Wang. Content-based image retrieval - a survey on the approaches and trends of the new age. In Proc. MIR Workshop, ACM Multimedia, 2005.
[60] R. Datta, J. Li, and J. Z. Wang. IMAGINATION: A robust image-based captcha generation system. In Proc. ACM Multimedia, 2005.
[61] R. Datta, J. Li, and J.Z. Wang. Imagination: A robust image-based captcha generation system. In Proc. ACM Multimedia, 2005.
[62] R. Datta, J. Li, and J.Z. Wang. Learning the consensus on visual quality for next-generation image management. In Proc. ACM Multimedia, 2007.
[63] I. Daubechies. Ten Lectures on Wavelets. SIAM, Philadelphia, 1992. [64] V. de Silva and J. Tenenbaum. Global versus local methods in nonlinear dimensionality reduction. In Proc. NIPS, 2003.
[65] A. Del Bimbo and P. Pala. Visual image retrieval by elastic matching of user sketches. IEEE Trans. Pattern Analysis and Machine Intelligence, 19(2):121– 132, 1997.
[66] Y. Deng and B. Manjunath. Unsupervised segmentation of color-texture regions in images and video. IEEE Trans. Pattern Analysis and Machine Intelligence, 23(8):800–810, 2001.
[67] Y. Deng, B. S. Manjunath, C. Kenney, M. S. Moore, and H. Shin. An efficient color representation for image retrieval. IEEE Trans. Image Processing, 10(1):140–147, 2001.
[68] Discovery. Digital pics ’read’ by computer. Discovery News, 11/09/2006, 2006.
[69] M. N. Do and M. Vetterli. Wavelet-based texture retrieval using generalized gaussian density and kullback-leibler distance. IEEE Trans. Image Processing, 11(2):146–158, 2002.
[70] A. Dong and B. Bhanu. Active concept learning for image retrieval in dynamic databases. In Proc. IEEE ICCV, 2003.
[71] DPChallenge, 2006. http://www.dpchallenge.com.
[72] Y. Du and J. Z. Wang. A scalable integrated region-based image retrieval system. In Proc. IEEE ICIP, 2001.
[73] M. Dubinko, R. Kumar, J.Magnani, J. Novak, P. Raghavan, and A. Tomkins. Visualizing tags over time. ACM Transactions on the Web, 1(2), 2007.
[74] P. Duygulu, K. Barnard, N. de Freitas, and D. Forsyth. Object recognition as machine translation: Learning a lexicon for a fixed image vocabulary. In Proc. ECCV, 2002.
[75] Y. Eisenthal, G. Dror, and E. Ruppin. Facial attractiveness: Beauty and the machine. Neural Computation, 18(1):119–142, 2006.
[76] J. Elson, J.R. Douceur, J. Howell, and J. Saul. Asirra: A CAPTCHA that exploits interest-aligned manual image categorization. In Proc. ACM CCS, 2007.
[77] A.M. Eskicioglu and P.S. Fisher. Image quality measures and their performance. IEEE Trans. Communications, 45(12):2959–2965, 1995.
[78] R. Fagin. Combining fuzzy information from multiple systems. In Proc. PODS, 1997.
[79] Y. Fang and D. Geman. Experiments in mental face retrieval. In Proc. Audio and Video-based Biometric Person Authentication, 2005.
[80] Y. Fang, D. Geman, and N. Boujemaa. An interactive system for mental face retrieval. In Proc. MIR Workshop, ACM Multimedia, 2005.
[81] H. Feng, R. Shi, and T. S. Chua. A bootstrapping framework for annotating and retrieving www images. In Proc. ACM Multimedia, 2004.
[82] S. L. Feng, R. Manmatha, and V. Lavrenko. Multiple bernoulli relevance models for image and video annotation. In Proc. IEEE CVPR, 2004.
[83] R. Fergus, P. Perona, and A. Zisserman. Object class recognition by unsupervised scale-invariant learning. In Proc. IEEE CVPR, 2003.
[84] R. Fergus, P. Perona, and A. Zisserman. A sparse object category model for efficient learning and exhaustive recognition. In Proc. IEEE CVPR, 2005.
[85] G.D. Finlayson. Color in perspective. IEEE Trans. Pattern Analysis and Machine Intelligence, 18(10):1034–1038, 1996.
[86] F. Fleuret and D. Geman. Stationary features and cat detection. J. Machine Learning Research, 9:2549–2578, 2008.
[87] M. Flickner, H. Sawhney, W. Niblack, J. Ashley, Q. Huang, B. Dom, M. Gorkani, J. Hafner, D. Lee, D. Petkovic, D. Steele, and P. Yanker. Query by image and video content: The QBIC system. IEEE Computer, 28(9):23– 32, 1995.
[88] Flickr, 2006. http://www.flickr.com.
[89] R.W. Floyd and L. Steinberg. An adaptive algorithm for spatial grey scale. In Proc. Society of Information Display, 1976.
[90] Y. Freund and R. E. Schapire. Experiments with a new boosting algorithm. In Proc. ICML, 1996.
[91] B. Gao, T.-Y. Liu, T. Qin, X. Zheng, Q.-S. Cheng, and W.-Y. Ma. Web image clustering by consistent utilization of visual features and surrounding texts. In Proc. ACM Multimedia, 2005.
[92] Y. Gao, J. Fan, H. Luo, X. Xue, and R. Jain. Automatic image annotation by incorporating feature hierarchy and boosting to scale up SVM classifiers. In Proc. ACM Multimedia, 2006.
[93] N. Garg and I.Weber. Personalized tag suggestion for flickr. In Proc. WWW, 2008.
[94] T. Gevers and A.W.M. Smeulders. Pictoseek: Combining color and shape invariant features for image retrieval. IEEE Trans. Image Processing, 9(1):102– 119, 2000.
[95] GlobalMemoryNet, 2006. http://www.memorynet.org.
[96] K.-S. Goh, E. Y. Chang, and K.-T. Cheng. SVM binary classifier ensembles for image classification. In Proc. ACM CIKM, 2001.
[97] K.-S. Goh, E. Y. Chang, and W.-C. Lai. Multimodal concept-dependent active learning for image retrieval. In Proc. ACM Multimedia, 2004.
[98] P. Golle. Machine learning attacks against the asirra captcha. In Proc. ACM CCS, 2008.
[99] G. H. Golub and C. F. Van Loan. Matrix Computations. Johns Hopkins University Press, Baltimore, Maryland, 1983.
[100] Google Scholar, 2006. http://scholar.google.com.
[101] S. Gordon, H. Greenspan, and J. Goldberger. Applying the information bottleneck principle to unsupervised clustering of discrete and continuous image representations. In Proc. IEEE ICCV, 2003.
[102] V. Gouet and N. Boujemaa. On the robustness of color points of interest for image retrieval. In Proc. IEEE ICIP, 2002.
[103] K. Grauman and T. Darrell. Efficient image matching with distributions of local invariant features. In Proc. IEEE CVPR, 2005.
[104] Guardian. How captcha was foiled: Are you a man or a mouse?, 2008. http: //www.guardian.co.uk/technology/2008/aug/28/internet.captcha.
[105] A. Gupta and R. Jain. Visual information retrieval. Communications of the ACM, 40(5):70–79, 1997.
[106] I. Guyon and A. Elisseeff. An introduction to variable and feature selection. J. Machine Learning Research, 3:1157–1182, 2003.
[107] E. Hadjidemetriou, M. D. Grossberg, and S. K. Nayar. Multiresolution histograms and their use for recognition. IEEE Trans. Pattern Analysis and Machine Intelligence, 26(7):831–847, 2004.
[108] J. Han, K. N. Ngan, M. Li, and H.-J. Zhang. A memory learning framework for effective image retrieval. IEEE Trans. Image Processing, 14(4):511–524, 2005.
[109] R.M. Haralick. Statistical and structural approaches to texture. Proc. IEEE, 67(5):786–804, 1979.
[110] T. Hastie, R. Tibshirani, and J.H. Friedman. The Elements of Statistical Learning. Springer-Verlag, 2001.
[111] A. G. Hauptmann and M. G. Christel. Successful approaches in the TREC video retrieval evaluations. In Proc. ACM Multimedia, 2004.
[112] J. He, M. Li, H.-J. Zhang, H. Tong, and C. Zhang. Manifold-ranking based image retrieval. In Proc. ACM Multimedia, 2004.
[113] J. He, M. Li, H.-J. Zhang, H. Tong, and C. Zhang. Mean version space: a new active learning method for content-based image retrieval. In Proc. MIR Workshop, ACM Multimedia, 2004.
[114] X. He. Incremental semi-supervised subspace learning for image retrieval. In Proc. ACM Multimedia, 2004.
[115] X. He, W.-Y. Ma, and H.-J. Zhang. Learning an image manifold for retrieval. In Proc. ACM Multimedia, 2004.
[116] T.K. Ho, J.J. Hull, and S.N. Srihari. Decision combination in multiple classifier systems. IEEE Trans. Pattern Analysis and Machine Intelligence, 16(1):66–75, 1994.
[117] C.-H. Hoi and M. R. Lyu. Group-based relevance feedback with support vector machine ensembles. In Proc. IEEE ICPR, 2004.
[118] C.-H. Hoi and M. R. Lyu. A novel log-based relevance feedback technique in content-based image retrieval. In Proc. ACM Multimedia, 2004.
[119] D. Hoiem, R. Sukthankar, H. Schneiderman, and L. Huston. Object-based image retrieval using the statistical structure of images. In Proc. IEEE CVPR, 2004.
[120] Jing Huang, S. Ravi Kumar, M. Mitra, W.-J. Zhu, and R. Zabih. Spatial color indexing and applications. Intl. J. Computer Vision, 35(3):245–268, 1999.
[121] D. P. Huijsmans and N. Sebe. How to complete performance graphs in content-based image retrieval: Add generality and normalize scope. IEEE Trans. Pattern Analysis and Machine Intelligence, 27(2):245–251, 2005.
[122] Q. Iqbal and J. K. Aggarwal. Retrieval by classification of images containing large manmade objects using perceptual grouping. Pattern Recognition J., 35(7):1463–1479, 2002.
[123] A. Jaimes, K. Omura, T. Nagamine, and K. Hirata. Memory cues for meeting video retrieval. In Proc. CARPE Workshop, ACM Multimedia, 2004.
[124] A.K. Jain and R.C. Dubes. Algorithms for Clustering Data. Prentice Hall, 1988.
[125] A.K. Jain and F. Farrokhnia. Unsupervised texture segmentation using gabor filters. In Proc. Intl. Conf. Systems, Man and Cybernetics, 1990.
[126] B. J. Jansen, A. Spink, and J. Pedersen. An analysis of multimedia searching on Altavista. In Proc. MIR Workshop, ACM Multimedia, 2003.
[127] J. Jeon, V. Lavrenko, and R. Manmatha. Automatic image annotation and retrieval using cross-media relevance models. In Proc. ACM SIGIR, 2003.
[128] S. Jeong, C. S. Won, and R.M. Gray. Image retrieval using color histograms generated by gauss mixture vector quantization. Computer Vision and Image Understanding, 9(1–3):44–66, 2004.
[129] R. Jin, J. Y. Chai, and L. Si. Effective automatic image annotation via a coherent language model and active learning. In Proc. ACM Multimedia, 2004.
[130] R. Jin and A.G. Hauptmann. Using a probabilistic source model for comparing images. In Proc. IEEE ICIP, 2002.
[131] Y. Jin, L. Khan, L. Wang, and M. Awad. Image annotations by combining multiple evidence & wordnet. In Proc. ACM Multimedia, 2005.
[132] F. Jing, M. Li, H.-J. Zhang, and B. Zhang. An efficient and effective regionbased image retrieval framework. IEEE Trans. Image Processing, 13(5):699– 709, 2004.
[133] F. Jing, M. Li, H.-J. Zhang, and B. Zhang. Relevance feedback in regionbased image retrieval. IEEE Trans. Circuits and Systems for Video Technology, 14(5):672–681, 2004.
[134] F. Jing, M. Li, H. J. Zhang, and B. Zhang. A unified framework for image retrieval using keyword and visual features. IEEE Trans. Image Processing, 14(6), 2005.
[135] F. Jing, C. Wang, Y. Yao, K. Deng, L. Zhang, and W. Y. Ma. IGroup: Web image search results clustering. In Proc. ACM Multimedia, 2006.
[136] D. Joshi, R. Datta, Z. Zhuang, W.P. Weiss, M. Friedenberg, J.Z. Wang, and J. Li. Paragrab: A comprehensive architecture for web image management and multimodal querying, 2006.
[137] D. Joshi, M. Naphade, and A. Natsev. A greedy performance driven algorithm for decision fusion learning. In IEEE ICIP, 2007.
[138] Y. Ke, R. Sukthankar, and L. Huston. Efficient near-duplicate detection and subimage retrieval. In Proc. ACM Multimedia, 2004.
[139] Y. Ke, R. Sukthankar, and L. Huston. Efficient near-duplicate detection and subimage retrieval. In Proc. ACM Multimedia, 2004.
[140] Y. Ke, X. Tang, and F. Jing. The design of high-level features for photo quality assessment. In Proc. IEEE CVPR, 2006.
[141] Y. Ke, X. Tang, and F. Jing. The design of high-level features for photo quality assessment. In Proc. IEEE CVPR, 2006.
[142] M. L. Kherfi, D. Ziou, and A. Bernardi. Image retrieval from the world wide web: Issues, techniques, and systems. ACM Computing Surveys, 36(1):35–67, 2004.
[143] D.-H. Kim and C.-W. Chung. Qcluster: Relevance feedback using adaptive clustering for content based image retrieval. In Proc. ACM SIGMOD, 2003.
[144] Y. S. Kim, W. N. Street, and F. Menczer. Feature selection in unsupervised learning via evolutionary search. In Proc. ACM SIGKDD, 2000.
[145] B. Ko and H. Byun. Integrated region-based image retrieval using region’s spatial relationships. In Proc. IEEE ICPR, 2002.
[146] J.Z. Kolter and M.A. Maloof. Dynamic weighted majority: An ensemble method for drifting concepts. JMLR, 8:2755–2790, 2007.
[147] R.E. Korf. Optimal rectangle packing: New results. In Proc. ICAPS, 2004.
[148] J. Laaksonen, M. Koskela, S. Laakso, and E. Oja. Self-organizing maps as a relevance feedback technique in content-based image retrieval. Pattern Analysis and Applications, 4:140–152, 2001.
[149] J. Laaksonen, M. Koskela, and E. Oja. PicSOM: Self-organizing image retrieval with MPEG-7 content descriptors. IEEE Trans. Neural Networks, 13(4):841–853, 2002.
[150] L. J. Latecki and R. Lakamper. Shape similarity measure based on correspondence of visual parts. IEEE Trans. Pattern Analysis and Machine Intelligence, 22(10):1185–1190, 2000.
[151] V. Lavrenko, R. Manmatha, and J. Jeon. A model for learning the semantics of pictures. In Proc. NIPS, 2003.
[152] S. Lazebnik, C. Schmid, and J. Ponce. Affine-invariant local descriptors and neighborhood statistics for texture recognition. In Proc. IEEE ICCV, 2003.
[153] C. Leacock and M. Chodorow. Combining local context and wordnet similarity for word sense identification. C. Fellbaum, Ed., WordNet: An Electronic Lexical Database, pages 265–283, 1998.
[154] D.B. Lenat. Cyc: A large-scale investment in knowledge infrastructure. Comm. of the ACM, 38(11):33–38, 1995.
[155] M. Lesk. How much information is there in the world?, 1997. http://www. lesk.com/mlesk/ksg97/ksg.html.
[156] E. Levina and P. Bickel. The earth mover’s distance is the mallows distance: Some insights from statistics. In Proc. IEEE ICCV, 2001.
[157] M. Lew, N. Sebe, C. Djeraba, and R. Jain. Content-based multimedia information retrieval: State-of-the-art and challenges. ACM Trans. Multimedia Computing, Communication, and Applications, 2(1):1–19, 2006.
[158] B. Li, K.-S. Goh, and E. Y. Chang. Confidence-based dynamic ensemble for image annotation and semantics discovery. In Proc. ACM Multimedia, 2003.
[159] J. Li. A mutual semantic endorsement approach to image retrieval and context provision. In Proc. MIR Workshop, ACM Multimedia, 2005.
[160] J. Li. Two-scale image retrieval with significant meta-information feedback. In Proc. ACM Multimedia, 2005.
[161] J. Li, R. M. Gray, and R. A. Olshen. Multiresolution image classification by hierarchical modeling with two dimensional hidden markov models. IEEE Trans. Information Theory, 46(5):1826–1841, 2000.
[162] J. Li, A. Najmi, and R. M. Gray. Image classification by a two dimensional hidden markov model. IEEE Trans. Signal Processing, 48(2):527–533, 2000.
[163] J. Li and J. Z.Wang. Automatic linguistic indexing of pictures by a statistical modeling approach. IEEE Trans. Pattern Analysis and Machine Intelligence, 25(9):1075–1088, 2003. [164] J. Li and J. Z. Wang. Studying digital imagery of ancient paintings by mixtures of stochastic models. IEEE Trans. Image Processing, 13(3):340– 353, 2004. [165] J. Li, J. Z. Wang, and G. Wiederhold. IRM: Integrated region matching for image retrieval. In Proc. ACM Multimedia, 2000.
[166] J. Li and J.Z. Wang. Real-time computerized annotation of pictures. IEEE Trans. Pattern Analysis and Machine Intelligence, 30(6):985–1002, 2008.
[167] X. Li, L. Chen, L. Zhang, F. Lin, and W. Y. Ma. Image annotation by large-scale content-based image retrieval. In Proc. ACM Multimedia, 2006.
[168] Y. Li, L.G. Shaprio, and J.A. Bilmes. Generative/discriminative learning algorithm for image classification. In Proc. ICCV, 2005.
[169] Z.-W. Li, X. Xie, H. Liu, X. Tang, M. Li, and W.-Y. Ma. Intuitive and effective interfaces for www image search engines. In Proc. ACM Multimedia, 2004.
[170] Y.-Yu Lin, T.-L. Liu, and H.-T. Chen. Semantic manifold learning for image retrieval. In Proc. ACM Multimedia, 2005.
[171] W. Liu and X. Tang. Learning an image-word embedding for image autoannotation on the nonlinear latent space. In Proc. ACM Multimedia, 2005.
[172] D.G. Lowe. Object recognition from local scale-invariant features. In Proc. ICCV, 1999.
[173] Y. Lu, C. Hu, X. Zhu, H.J. Zhang, and Q. Yang. A unified framework for semantics and feature based relevance feedback in image retrieval systems. In Proc. ACM Multimedia, 2000.
[174] P. Lyman and H. Varian. How much information?, 2003. http://www2. sims.berkeley.edu/research/projects/how-much-info-2003/.
[175] W.-Y. Ma and B.S. Manjunath. Texture thesaurus for browsing large aerial photographs. J. American Society for Information Science, 49(7):633–648, 1998.
[176] W.Y. Ma and B.S. Manjunath. Netra: A toolbox for navigating large image databases. In Proc. IEEE ICIP, 1997.
[177] W.Y. Ma and B.S. Manjunath. Netra: A toolbox for navigating large image databases. IEEE Trans. Pattern Analysis and Machine Intelligence, 7(3):184–198, 1999.
[178] J. Malik, S. Belongie, T. K. Leung, and J. Shi. Contour and texture analysis for image segmentation. Intl. J. Computer Vision, 43(1):7–27, 2001.
[179] J. Malik and P. Perona. Preattentive texture discrimination with early vision mechanisms. J. Optical Society of America A, 7(5):923–932, 1990.
[180] C. L. Mallows. A note on asymptotic joint normality. Annals of Mathematical Statistics, 43(2):508–515, 1972.
[181] C.L. Mallows. A note on asymptotic joint normality. Annals of Mathematical Statistics, 43(2):508–515, 1972.
[182] M.A. Maloof and R.S. Michalski. Incremental learning with partial instance memory. Artificial Intelligence, 154:95–126, 2004.
[183] B. S. Manjunath, J.-R. Ohm, V. V. Vasudevan, and A. Yamada. Color and texture descriptors. IEEE Trans. Circuits and Systems for Video Technology, 11(6):703–715, 2001.
[184] B.S. Manjunath and W.-Y. Ma. Texture features for browsing and retrieval of image data. IEEE Trans. Pattern Analysis and Machine Intelligence, 18(8):837–842, 1996.
[185] J. R. Mathiassen, A. Skavhaug, and K. Bo. Texture similarity measure using kullback-leibler divergence between gamma distributions. In Proc. ECCV, 2002.
[186] G. McLachlan and D. Peel. Finite Mixture Models. Wiley-Interscience, 2000.
[187] R. Mehrotra and J. E. Gary. Similar-shape retrieval in shape data management. IEEE Computer, 28(9):57–62, 1995.
[188] K. Mikolajczk and C. Schmid. A performance evaluation of local descriptors. In Proc. IEEE CVPR, 2003.
[189] K. Mikolajczyk and C. Schmid. Scale and affine invariant interest point detectors. Intl. J. Computer Vision, 60(1):63–86, 2004.
[190] G. Miller. Wordnet: A lexical database for english. Communications of the ACM, 38(11):39–41, 1995.
[191] P. Mitra, C.A. Murthy, and S.K. Pal. Unsupervised feature selection using feature similarity. IEEE Trans. Pattern Analysis and Machine Intelligence, 24(3):301–312, 2002.
[192] F. Mokhtarian. Silhouette-based isolated object recognition through curvature scale space. IEEE Trans. Pattern Analysis and Machine Intelligence, 17(5):539–544, 1995.
[193] F. Monay and D. Gatica-Perez. On image auto-annotation with latent space models. In Proc. ACM Multimedia, 2003.
[194] W.G. Morein, A. Stavrou, D.L. Cook, V. Mishra Keromytis, and D. Rubenstein. Using graphic turing tests to counter automated ddos attacks against web servers. In Proc. ACM CCS, 2003.
[195] G. Mori and J. Malik. Recognizing objects in adversarial clutter: Breaking a visual CAPTCHA. In Proc. IEEE CVPR, 2003.
[196] G. Moy, N. Jones, C. Harkless, and R. Potter. Distortion estimation techniques in solving visual CAPTCHAs. In Proc. IEEE CVPR, 2004.
[197] S. Mukherjea, K. Hirata, and Y. Hara. Amore: A world wide web image retrieval engine. In Proc. WWW, 1999.
[198] H. Muller, N. Michoux, D. Bandon, and A. Geissbuhler. A review of contentbased image retrieval systems in medical applications - clinical benefits and future directions. Intl. J. Medical Informatics, 73(1):1–23, 2004.
[199] H. Muller, T. Pun, and D. Squire. Learning from user behavior in image retrieval: Application of market basket analysis. Intl. J. Computer Vision, 56(1/2):65–77, 2004.
[200] T. Nagamine, A. Jaimes, K. Omura, and K. Hirata. A visuospatial memory cue system for meeting video retrieval. In Proc. ACM Multimedia (Demonstration), 2004.
[201] M. Nakazato, C. Dagli, and T.S. Huang. Evaluating group-based relevance feedback for content-based image retrieval. In Proc. IEEE ICIP, 2003.
[202] A. Natsev, M. R. Naphade, and J. Tesic. Learning the semantics of multimedia queries and concepts from a small number of examples. In Proc. ACM Multimedia, 2005.
[203] A. Natsev, R. Rastogi, and K. Shim. Walrus: A similarity retrieval algorithm for image databases. IEEE Trans. Knowledge and Data Engineering, 16(3):301–316, 2004.
[204] A. Natsev and J.R. Smith. A study of image retrieval by anchoring. In Proc. IEEE ICME, 2002.
[205] T.-T. Ng, S.-F. Chang, J. Hsu, L. Xie, and M.-P. Tsui. Physics-motivated features for distinguishing photographic images and computer graphics. In Proc. ACM Multimedia, 2005.
[206] Hot or Not, 2006. http://www.hotornot.com.
[207] T. H. Painter, J. Dozier, D. A. Roberts, R. E. Davis, and R. O. Green. Retrieval of subpixel snow-covered area and grain size from imaging spectrometer data. Remote Sensing of Environment, 85(1):64–77, 2003.
[208] N. Panda and E. Y. Chang. Efficient top-k hyperplane query processing for multimedia information retrieval. In Proc. ACM Multimedia, 2006.
[209] A. Pentland, R.W. Picard, and S. Sclaroff. Photobook: Tools for contentbased manipulation of image databases. In Proc. SPIE, 1994. 238
[210] G. Petraglia, M. Sebillo, M. Tucci, and G. Tortora. Virtual images for similarity retrieval in image databases. IEEE Trans. Knowledge and Data Engineering, 13(6):951–967, 2001.
[211] E. G. M. Petrakis, A. Diplaros, and E. Milios. Matching and retrieval of distorted and occluded shapes using dynamic programming. IEEE Trans. Pattern Analysis and Machine Intelligence, 24(4):509–522, 2002.
[212] E.G.M. Petrakis and A. Faloutsos. Similarity searching in medical image databases. IEEE Trans. Knowledge and Data Engineering, 9(3):435–447, 1997.
[213] Photo.net, 2006. http://photo.net.
[214] Photo.net. Rating system, 2006. http://www.photo.net/gallery/ photocritique/standards/.
[215] M. Pi, M. K. Mandal, and A. Basu. Image retrieval based on histogram of fractal parameters. IEEE Trans. Multimedia, 7(4):597–605, 2005.
[216] J. Portilla and E.P. Simoncelli. A parametric texture model based on joint statistics of complex wavelet coefficients. Intl. J. Computer Vision, 40(1):49– 71, 2000.
[217] Oxford University Press. Oxford advanced learner’s dictionary, 2006.
[218] T. Quack, U. Monich, L. Thiele, and B. S. Manjunath. Cortina: A system for largescale, content-based web image retrieval. In Proc. ACM Multimedia, 2004.
[219] P. Resnick and H.R. Varian. Recommender systems. Communications of the ACM, 40(3):56–58, 1997.
[220] N. C. Rowe. Marie-4: A high-recall, self-improving web crawler that finds images using captions. IEEE Intelligent Systems, 17(4):8–14, 2002.
[221] Y. Rubner, C. Tomasi, and L.J. Guibas. The earth mover’s distance as a metric for image retrieval. Intl. J. Computer Vision, 40(2):99–121, 2000.
[222] Y. Rui and T. S. Huang. Optimizing learning in image retrieval. In Proc. IEEE CVPR, 2000.
[223] Y. Rui, T. S. Huang, M. Ortega, and S. Mehrotra. Relevance feedback: A power tool in interactive content-based image retrieval. IEEE Trans. Circuits
[224] Y. Rui, T.S. Huang, and S.-F. Chang. Image retrieval: Current techniques, promising directions and open issues. J. Visual Communication and Image Representation, 10(1):39–62, 1999.
[225] Y. Rui, T.S. Huang, and S. Mehrotra. Content-based image retrieval with relevance feedback in mars. In Proc. IEEE ICIP, 1997.
[226] Y. Rui and Z. Liu. ARTiFACIAL: Automated reverse turing test using facial features. In Proc. ACM Multimedia, 2003.
[227] J. Saul. Petfinder, 2009. http://www.petfinder.com.
[228] B. Le Saux and N. Boujemaa. Unsupervised robust clustering for image database categorization. In Proc. IEEE ICPR, 2002.
[229] F. Schaffalitzky and A. Zisserman. Viewpoint invariant texture matching andwide baseline stereo. In Proc. IEEE ICCV, 2001.
[230] J.C. Schlimmer and R.H. Granger. Beyond incremental processing: Tracking concept drift. In Proc. AAAI, 1986.
[231] C. Schmid and R. Mohr. Local grayvalue invariants for image retrieval. IEEE Trans. Pattern Analysis and Machine Intelligence, 19(5):530–535, 1997.
[232] M. Schroder, H. Rehrauer, K. Seidel, and M. Datcu. Interactive learning and probabilistic retrieval in remote sensing image archives. IEEE Trans. Geoscience and Remote Sensing, 38(5):2288–2298, 2000.
[233] ScientificAmerican. Computers get the picture. Steve Mirsky - Scientific American 60-second World of Science, 11/06/2006, 2006.
[234] N. Sebe, M. S. Lew, and D. P. Huijsmans. Toward improved ranking metrics. IEEE Trans. Pattern Analysis and Machine Intelligence, 22(10):1132–1141, 2000.
[235] N. Sebe, M. S. Lew, X. Zhou, T. S. Huang, and E. Bakker. The state of the art in image and video retrieval. In Proc. CIVR, 2003.
[236] H.R. Sheikh, A.C. Bovik, and L. Cormack. No-reference quality assessment using natural scene statistics: Jpeg2000. IEEE Trans. Image Processing, 14(11):1918–1927, 2005.
[237] J. Shi and J. Malik. Normalized cuts and image segmentation. IEEE Trans. Pattern Analysis and Machine Intelligence, 22(8):888–905, 2000. [238] B. Sigurbjornsson and R. van Zwol. Flickr tag recommendation based on collective knowledge. In Proc. WWW, 2008.
[239] D. Silver, G. Bakir, K. Bennett, R. Caruana, M. Pontil, S. Russell, and P. Tadepalli. Inductive transfer: 10 years later. In Intl. Workshop at NIPS, 2005.
[240] Slashdot. Searching by image instead of keywords, 2005. http://slashdot. org/articles/05/05/04/2239224.shtml.
[241] Slashdot. Yahoo CAPTCHA hacked, 2008. http://it.slashdot.org/it/ 08/01/30/0037254.shtml.
[242] A. W. Smeulders, M. Worring, S. Santini, A. Gupta, , and R. Jain. Contentbased image retrieval at the end of the early years. IEEE Trans. Pattern Analysis and Machine Intelligence, 22(12):1349–1380, 2000.
[243] A. W. Smeulders, M. Worring, S. Santini, A. Gupta, , and R. Jain. Contentbased image retrieval at the end of the early years. IEEE Trans. Pattern Analysis and Machine Intelligence, 22(12):1349–1380, 2000.
[244] J.R. Smith and S.-F. Chang. Integrated spatial and feature image query. IEEE Trans. Knowledge and Data Engineering, 9(3):435–447, 1997.
[245] J.R. Smith and S.-F. Chang. Visualseek: a fully automated content-based image query system. In Proc. ACM Multimedia, 1997.
[246] B. Smolka, M. Szczepanski, R Lukac, and A. N. Venetsanopoulos. Robust color image retrieval for the world wide web. In Proc. IEEE ICASSP, 2004.
[247] C. G. M. Snoek and M. Worring. Multimodal video indexing: A review of the state-of-the-art. Multimedia Tools and Applications, 25(1):5–35, 2005.
[248] Z. Su, H.-J. Zhang, S. Li, and S. Ma. Relevance feedback in content-based image retrieval: Bayesian framework, feature subspaces, and progressive learning. IEEE Trans. Image Processing, 12(8):924–937, 2003.
[249] M.J. Swain and B.H. Ballard. Color indexing. Intl. J. Computer Vision, 7(1):11–32, 1991.
[250] D.L. Swets and J. Weng. Using discriminant eigenfeatures for image retrieval. IEEE Trans. Pattern Analysis and Machine Intelligence, 18(8):831– 836, 1996.
[251] Terragalleria, 2006. http://terragalleria.com.
[252] A. Thayananthan, B. Stenger, P.H.S. Torr, and R. Cipolla. Shape context and chamfer matching in cluttered scenes. In Proc. IEEE CVPR, 2003.
[253] C. Theoharatos, N. A. Laskaris, G. Economou, and S. Fotopoulos. A generic scheme for color image retrieval based on the multivariate wald-wolfowitz test. IEEE Trans. Knowledge and Data Engineering, 17(6):808–819, 2005.
[254] T.M. Therneau and E.J. Atkinson. An introduction to recursive partitioning using rpart routines. In Technical Report, Mayo Foundation, 1997.
[255] Q. Tian, N. Sebe, M. S. Lew, E. Loupias, and T. S. Huang. Image retrieval using wavelet-based salient points. J. Electronic Imaging, 10(4):835–849, 2001.
[256] K. Tieu and P. Viola. Boosting image retrieval. Intl. J. Computer Vision, 56(1/2):17–36, 2004.
[257] N. Tishby, F.C. Pereira, and W. Bialek. The information botflencek method. In Proc. Allerton Conf. Communication and Computation, 1999.
[258] H. Tong, M. Li, H. Zhang, J. He, and C. Zhang. Classification of digital photos taken by photographers or home users. In Proc. Pacific Rim Conference on Multimedia, 2004.
[259] S. Tong and E. Chang. Support vector machine active learning for image retrieval. In Proc. ACM Multimedia, 2001.
[260] Z. Tu and S.-C. Zhu. Image segmentation by data-driven markov chain monte carlo. IEEE Trans. Pattern Analysis and Machine Intelligence, 24(5):657– 673, 2002.
[261] A. Turing. Computing machinery and intelligence. Mind, 59(236):433–460, 1950.
[262] T. Tuytelaars and L. van Gool. Content-based image retrieval based on local affinely invariant regions. In Proc. VISUAL, 1999.
[263] M. Unser. Texture classification and segmentation using wavelet frames. IEEE Trans. Image Processing, 4(11):1549–1560, 1995.
[264] A. Vailaya, M. A. T. Figueiredo, A. K. Jain, and H.-J. Zhang. Image classification for content-based indexing. IEEE Trans. Image Processing, 10(1):117– 130, 2001.
[265] V. Vapnik. The Nature of Statistical Learning Theory. Springer, 1995.
[266] N. Vasconcelos. On the efficient evaluation of probabilistic similarity functions for image retrieval. IEEE Trans. Information Theory, 50(7):1482–1496, 2004.
[267] N. Vasconcelos and A. Lippman. Learning from user feedback in image retrieval systems. In Proc. NIPS, 2000.
[268] N. Vasconcelos and A. Lippman. A probabilistic architecture for contentbased image retrieval. In Proc. IEEE CVPR, 2000.
[269] N. Vasconcelos and A. Lippman. A multiresolution manifold distance for invariant image similarity. IEEE Trans. Multimedia, 7(1):127–142, 2005.
[270] VBulletin. Nospam! an alternative to captcha images, 2008. http://www. vbulletin.org/forum/showthread.php?t=124828.
[271] A. Velivelli, C.-W. Ngo, and T. S. Huang. Detection of documentary scene changes by audio-visual fusion. In Proc. CIVR, 2004.
[272] R. Vilalta and Y. Drissi. A perspective view and survey of meta-learning. AI Review, 18(2):77–95, 2002.
[273] T. Volkmer, J. R. Smith, and A. Natsev. A web-based system for collaborative annotation of large image and video collections: An evaluation and user study. In Proc. ACM Multimedia, 2005.
[274] L. von Ahn, M. Blum, and J. Langford. Telling humans and computers apart (automatically) or how lazy cryptographers do ai. Communications of the ACM, 47(2):57–60, 2004.
[275] C. Wang, F. Jing, L. Zhang, and H.-J. Zhang. Image annotation refinement using random walk with restarts. In Proc. ACM Multimedia, 2006.
[276] J. Z. Wang, N. Boujemaa, A. Del Bimbo, D. Geman, A. Hauptmann, and J. Tesic. Diversity in multimedia information retrieval research. In Proc. MIR Workshop, ACM Multimedia, 2006.
[277] J. Z. Wang, J. Li, R. M. Gray, and G. Wiederhold. Unsupervised multiresolution segmentation for images with low depth of field. IEEE Trans. Pattern Analysis and Machine Intelligence, 23(1):85–90, 2001.
[278] J.Z. Wang, J. Li, and G. Wiederhold. SIMPLIcity: Semantics-sensitive integrated matching for picture libraries. IEEE Trans. Pattern Analysis and Machine Intelligence, 23(9):947–963, 2001.
[279] J.Z. Wang, G.Wiederhold, O. Firschein, and S.X. Wei. Content-based image indexing and searching using daubechies’ wavelets. Intl. J. Digital Libraries, 1(4):311–328, 1998.
[280] X.-J. Wang, W.-Y. Ma, Q.-C. He, and X. Li. Grouping web image search result. In Proc. ACM Multimedia, 2004.
[281] X. J. Wang, W. Y. Ma, G. R. Xue, and X. Li. Multi-model similarity propagation and its application for web image retrieval. In Proc. ACM Multimedia, 2004.
[282] X.-J. Wang, L. Zhang, X. Li, and W.-Y. Ma. Annotating images by mining image search results. IEEE Trans. Pattern Analysis and Machine Intelligence, 30(11):1919–1932, 2008.
[283] Y. H. Wang. Image indexing and similarity retrieval based on spatial relationship model. Information Sciences - Informatics and Computer Science, 154(1-2):39–58, 2003.
[284] Z. Wang, Z. Chi, and D. Feng. Fuzzy integral for leaf image retrieval. In Proc. IEEE Intl. Conf. Fuzzy Systems, 2002.
[285] M. Webe, M. Welling, and P. Perona. Unsupervised learning of models for recognition. In Proc. ECCV, 2000.
[286] J. Weston, S. Mukherjee, O. Chapelle, M. Pontil, T. Poggio, and V. Vapnik. Feature selection for SVMs. In Proc. NIPS, 2000.
[287] G. Widmer. Tracking context changes through meta-learning. Machine Learning, 27(3):259–286, 1997.
[288] R.C. Wilson and E.R. Hancock. Structural matching by discrete relaxation. IEEE Trans. Pattern Analysis and Machine Intelligence, 19(6):634– 648, 1997.
[289] H.J. Wolfson and I. Rigoutsos. Geometric hashing: an overview. IEEE Trans. Computational Science and Engineering, 4(4):10–21, 1997.
[290] D. H.Wolpert. Stacked generalization. Neural Networks, 5(2):241–259, 1992.
[291] R.C.F. Wong and C.H.C. Leung. Automatic semantic annotation of realworld web images. IEEE Trans. Pattern Analysis and Machine Intelligence, 30(11):1933–1944, 2008.
[292] G. Wu, E. Y. Chang, and N. Panda. Formulating context-dependent similarity functions. In Proc. ACM Multimedia, 2005.
[293] H. Wu, H. Lu, and S. Ma. WillHunter: Interactive image retrieval with multilevel relevance measurement. In Proc. IEEE ICPR, 2004.
[294] P. Wu and B. S. Manjunath. Adaptive nearest neighbor search for relevance feedback in large image databases. In Proc. ACM Multimedia, 2001.
[295] W. Wu and J. Yang. SmartLabel: An object labeling tool using iterated harmonic energy minimization. In Proc. ACM Multimedia, 2006.
[296] Y. Wu, E. Y. Chang, K. C. C. Chang, and J. R. Smith. Optimal multimodal fusion for multimedia data analysis. In Proc. ACM Multimedia, 2004.
[297] Y. Wu, Q. Tian, and T. S. Huang. Discriminant-EM algorithm with application to image retrieval. In Proc. IEEE CVPR, 2000.
[298] E.P. Xing, A.Y. Ng, M.I. Jordan, and S. Russell. Distance metric learning, with application to clustering with side-information. In Proc. NIPS, 2003.
[299] C. Yang, M. Dong, and F. Fotouhi. Region based image annotation through multiple-instance learning. In Proc. ACM Multimedia, 2005.
[300] C. Yang, M. Dong, and F. Fotouhi. Semantic feedback for interactive image retrieval. In Proc. ACM Multimedia, 2005.
[301] K.-P. Yee, K. Swearingen, K. Li, and M. Hearst. Faceted metadata for image search and browsing. In Proc. ACM CHI, 2003.
[302] J. Yu, J. Amores, N. Sebe, and Q. Tian. Toward robust distance metric analysis for similarity estimation. In Proc. IEEE CVPR, 2006.
[303] S. X. Yu and J. Shi. Segmentation given partial grouping constraints. IEEE Trans. Pattern Analysis and Machine Intelligence, 26(2):173–183, 2004.
[304] Y. Zhai, A. Yilmaz, and M. Shah. Story segmentation in news videos using visual and textual cues. In Proc. ACM Multimedia, 2005.
[305] D.-Q. Zhang and S.-F. Chang. Detecting image near-duplicate by stochastic attributed relational graph matching with learning. In Proc. ACM Multimedia, 2004.
[306] H. Zhang, R. Rahmani, S. R. Cholleti, and S. A. Goldman. Local image representations using pruned salient points with applications to cbir. In Proc. ACM Multimedia, 2006.
[307] H. J. Zhang, L. Wenyin, and C. Hu. iFind - A system for semantics and feature based image retrieval over internet. In Proc. ACM Multimedia, 2000.
[308] L. Zhang, L. Chen, F. Jing, K. Deng, and W. Y. Ma. EnjoyPhoto - A vertical image search engine for enjoying high-quality photos. In Proc. ACM Multimedia, 2006.
[309] L. Zhang, L. Chen, M. Li, and H.-J. Zhang. Automated annotation of human faces in family albums. In Proc. ACM Multimedia, 2003.
[310] Q. Zhang, S. A. Goldman, W. Yu, and J. E. Fritts. Content-based image retrieval using multiple-instance learning. In Proc. ICML, 2002.
[311] R. Zhang and Z. Zhang. Hidden semantic concept discovery in region based image retrieval. In Proc. IEEE CVPR, 2004.
[312] Y. Zhang, M. Brady, and S. Smith. Segmentation of brain mr images through a hidden markov random field model and the expectation-maximization algorithm. IEEE Trans. Medical Imaging, 20(1):45–57, 2001.
[313] W. Zhao, R. Chellappa, P. J. Phillips, and A. Rosenfeld. Face recognition: A literature survey. ACM Computing Surveys, 35(4):399–458, 2003.
[314] B. Zheng, D. C. McClean, and X. Lu. Identifying biological concepts from a protein-related corpus with a probabilistic topic model. BMC Bioinformatics, 7(58), 2006.
[315] X. Zheng, D. Cai, X. He, W.-Y. Ma, and X. Lin. Locality preserving clustering for image database. In Proc. ACM Multimedia, 2004.
[316] D. Zhou, J. Weston, A. Gretton, O. Bousquet, and B. Scholkopf. Ranking on data manifolds. In Proc. NIPS, 2003.
[317] X. S. Zhou and T. S. Huang. Comparing discriminating transformations and SVM for learning during multimedia retrieval. In Proc. ACM Multimedia, 2001.
[318] X. S. Zhou and T. S. Huang. Small sample learning during multimedia retrieval using biasmap. In Proc. IEEE CVPR, 2001.
[319] X. S. Zhou and T. S. Huang. Unifying keywords and visual contents in image retrieval. IEEE Multimedia, 9(2):23–33, 2002.
[320] X. S. Zhou and T. S. Huang. Relevance feedback in image retrieval: A comprehensive review. Multimedia Systems, 8:536–544, 2003.
[321] L. Zhu, A. Zhang, A. Rao, and R. Srihari. Keyblock: An approach for content-based image retrieval. In Proc. ACM Multimedia, 2000.
[322] S.-C. Zhu and A. Yuille. Region competition: Unifying snakes, region growing, and bayes/mdl for multiband image segmentation. IEEE Trans. Pattern Analysis and Machine Intelligence, 18(9):884–900, 1996.
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