A genetic programming framework for content-based image retrieval
Inhaltsverzeichnis
Referenz
R.S. Torres, A.X. Falcão, M.A. Gonçalves, J.P.B.Z. Papa, W. Fan, E.A. Fox: A genetic programming framework for content-based image retrieval. Journal of the American Society for Information Science and Technology 42 (2) (2009) 283–292.
DOI
http://dx.doi.org/10.1016/j.patcog.2008.04.010
Abstract
The effectiveness of content-based image retrieval (CBIR) systems can be improved by combining image features or by weighting image similarities, as computed from multiple feature vectors. However, feature combination do not make sense always and the combined similarity function can be more complex than weight-based functions to better satisfy the users’ expectations. We address this problem by presenting a Genetic Programming framework to the design of combined similarity functions. Our method allows nonlinear combination of image similarities and is validated through several experiments, where the images are retrieved based on the shape of their objects. Experimental results demonstrate that the GP framework is suitable for the design of effective combinations functions.
Extended Abstract
Bibtex
@article{Torres2009283,
title = "A genetic programming framework for content-based image retrieval ",
journal = "Pattern Recognition ",
volume = "42",
number = "2",
pages = "283 - 292",
year = "2009",
note = "Learning Semantics from Multimedia Content ",
issn = "0031-3203",
doi = "http://dx.doi.org/10.1016/j.patcog.2008.04.010",
url = "http://www.sciencedirect.com/science/article/pii/S0031320308001623 http://dx.doi.org/10.1016/j.patcog.2008.04.010 ",
author = "Ricardo da S. Torres and Alexandre X. Falcão and Marcos A. Gonçalves and João P. Papa and Baoping Zhang and Weiguo Fan and Edward A. Fox",
keywords = "Content-based image retrieval",
keywords = "Genetic programming",
keywords = "Shape descriptors",
keywords = "Image analysis "
}
Used References
[1] R.S. Torres, A.X. Falcão, Content-based image retrieval: theory and applications, Rev. Inf. Teór. Apl. 13 (2) (2006) 161--185.
[2] R.S. Torres, A.X. Falcão, L. da F. Costa, A graph-based approach for multiscale shape analysis, Pattern Recognition 37 (6) (2004) 1163--1174.
[3] A.W.M. Smeulders, M. Worring, S. Santini, A. Gupta, R. Jain, Content-based image retrieval at the end of the years, IEEE TPAMI 22 (12) (2000) 1349--1380.
[4] N. Arica, F.T.Y. Vural, BAS: a perceptual shape descriptor based on the beam angle statistics, Pattern Recognition Lett. 24 (9--10) (2003) 1627--1639.
[5] L.J. Latecki, R. Lakamper, Shape similarity measure based on correspondence of visual parts, IEEE TPAMI 22 (10) (2000) 1185--1190.
[6] R.S. Torres, A.X. Falcão, Contour salience descriptors for effective image retrieval and analysis, Image Vision Comput. 25 (1) (2007) 3--13.
[7] Y.P. Wang, T. Pavlids, Optimal correspondence of string subsequences, IEEE TPAMI 12 (11) (1990) 1080--1087.
[8] K. Porkaew, S. Mehrotra, M. Ortega, K. Chakrabarti, Similarity search using multiple examples in MARS, in: Visual Information and Information Systems, Lecture Notes in Computer Science, vol. 1614, Springer, Amsterdam, 1999, pp. 68--75.
[9] M.S. Lew (Ed.), Principles of Visual Information Retrieval---Advances in Pattern Recognition, Springer, London/Berlin/Heidelberg, 2001.
[10] H. Shao, J.-W. Zhang, W.C. Cui, H. Zhao, Automatic feature weight assignment based on genetic algorithm for image retrieval, in: IEEE International Conference on Robotics, Intelligent Systems and Signal Processing, 2003, pp. 731--735.
[11] J.R. Koza, Genetic Programming: On the Programming of Computers by Means of Natural Selection, MIT Press, Cambridge, MA, 1992.
[12] B. Bhanu, Y. Lin, Object detection in multi-modal images using genetic programming, Appl. Soft Comput. 4 (2) (2004) 175--201.
[13] W. Fan, E.A. Fox, P. Pathak, H. Wu, The effects of fitness functions on genetic programming-based ranking discovery for web search, JASIST 55 (7) (2004) 628--636.
[14] B. Zhang, Intelligent fusion of structural and citation-based evidence for text classification, Ph.D. Thesis, Department of Computer Science, Virginia Polytechnic Institute and State University, 2006.
[15] Z. Steji � c, Y. Takama, K. Hirota, Mathematical aggregation operators in image retrieval: effect on retrieval performance and role in relevance feedback, Signal Processing 85 (2) (2005) 297--324.
[16] J.H. Holland, Adaptation in Natural and Artificial Systems, MIT Press, Cambridge, MA, 1992.
[17] W.B. Langdon, Data Structures and Genetic Programming: Genetic Programming+Data Structures=Automatic Programming!, Kluwer Academic Publishers, Dordrecht, 1998.
[18] W. Banzhaf, P. Nordin, R.E. Keller, F.D. Francone, Genetic Programming---An Introduction: On the Automatic Evolution of Computer Programs and its Applications, Morgan Kaufmann, San Francisco, CA, 1998.
[19] M. Swain, D. Ballard, Color Indexing, Int. J. Computer Vision 7 (1) (1991) 11--32. [20] R.C. Gonzalez, R.E. Woods, Digital Image Processing, Addison-Wesley, Reading, MA, 1992.
[21] M. Seshadri, Comprehensibility, overfitting and co-evolution in genetic programming for technical trading rules, Ph.D. Thesis, Worcester Polytechnic Institute, 2003.
[22] A. Lacerda, M. Cristo, M.A. Goncalves, W. Fan, N. Ziviani, B. Ribeiro-Neto, Learning to advertise, in: ACM SIGIR, 2006, pp. 549--556.
[23] R. Baeza-Yates, B. Ribeiro-Neto, Modern Information Retrieval, Addison-Wesley Longman Publishing Co. Inc., Boston, MA, 1999.
[24] P.C. Fishburn, Non-Linear Preference and Utility Theory, John Hopkins University Press, Baltimore, 1988.
[25] A. Frome, Y. Singer, J. Malik, Image retrieval and classification using distance functions, in: NIPS, 2006.
[26] S.D. MacArthur, C.E. Brodley, A.C. Kak, L.S. Broderick, Interactive content-based image retrieval using relevance feedback, Comput. Vision Image Understanding 88 (2) (2002) 55--75.
[27] Y. Rui, T.S. Huang, M. Ortega, S. Mehrotra, A power tool in interactive contentbased image retrieval, IEEE Tran. Circuits Syst. Video Technol. 8 (5) (1998) 644--655.
[28] Y.-Y. Lin, T.-L. Liu, C.-S. Fuh, Local ensemble kernel learning for object category recognition, in: CVPR, 2007, pp. 1--8.
[29] H. Zhang, A. Berg, M. Maire, J. Malik, Discriminative nearest neighbor classification for visual category recognition, in: CVPR, 2006, pp. 2126--2136.
[30] D. Howard, S.C. Roberts, R. Brankin, Target detection in SAR imagery by genetic programming, Adv. Eng. Software 30 (5) (1999) 303--311.
[31] Z. Sun, G. Bebis, R. Miller, Object detection using feature subset selection, Pattern Recognition 37 (11) (2004) 2165--2176.
[32] B. Bhanu, T. Lin, Genetic algorithm based feature selection in SAR images, Image Vision Comput. 21 (7) (2003) 591--608.
[33] D. Agnelli, A. Bollini, L. Lombardi, Image classification: an evolutionary approach, Pattern Recognition Lett. 26 (1--3) (2002) 303--309.
[34] C.D. Ferreira, R.S. Torres, Image retrieval with relevance feedback based on genetic programming, Technical Report IC-07-05, Institute of Computing, State University of Campinas, Feburary 2007.
[35] D. Tao, X. Tang, X. Li, Which components are important for interactive image searching, IEEE Trans. Circuits Syst. Video Technol. 18 (1) (2008) 3--11.
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