Genetic algorithm-based relevance feedback for image retrieval using local similarity patterns
Inhaltsverzeichnis
Referenz
Z. Steji , Y. Takama and K. Hirota: Genetic algorithm-based relevance feedback for image retrieval using local similarity patterns. Inf. Process. Manage., vol. 39, no. 1, pp. 1-23, 2003
DOI
http://dx.doi.org/10.1016/S0306-4573(02)00024-9
Abstract
Local similarity pattern (LSP) is proposed as a new method for computing image similarity. Similarity of a pair of images is expressed in terms of similarities of the corresponding image regions, obtained by the uniform partitioning of the image area. Different from the existing methods, each region-wise similarity is computed using a different combination of image features (color, shape, and texture). In addition, a method for optimizing the LSP-based similarity computation, based on genetic algorithm, is proposed, and incorporated in the relevance feedback mechanism, allowing the user to automatically specify LSP-based queries. LSP is evaluated on five test databases totalling around 2500 images of various sorts. Compared with both the conventional and the relevance feedback methods for computing image similarity, LSP brings in average over 11% increase in the retrieval precision. Results suggest that the proposed LSP method, allowing comparison of different image regions using different similarity criteria, is more suited for modeling the human perception of image similarity than the existing methods.
Extended Abstract
Bibtex
@article{Stejić20031,
title = "Genetic algorithm-based relevance feedback for image retrieval using local similarity patterns ",
journal = "Information Processing & Management ",
volume = "39",
number = "1",
pages = "1 - 23",
year = "2003",
note = "",
issn = "0306-4573",
doi = "http://dx.doi.org/10.1016/S0306-4573(02)00024-9",
url = "http://www.sciencedirect.com/science/article/pii/S0306457302000249",
author = "Zoran Stejić and Yasufumi Takama and Kaoru Hirota",
keywords = "Image retrieval",
keywords = "Image similarity",
keywords = "Relevance feedback",
keywords = "Genetic algorithm "
}
Used References
Brandt, S., Laaksonen, J., & Oja, E. (2000). Statistical shape features in content-based image retrieval. In Proceedings of the 15th International Conference on Pattern Recognition, ICPR-2000, Barcelona, Spain (Vol. 2) (pp. 1066–1069). Brodatz, P. (1966). Textures: A photographic album for artists and designers. New York: Dover Publications.
Chan, D. Y. M., & King, I. (1999a). Weight assignment in dissimilarity function for Chinese cursive script character image retrieval using genetic algorithm. In Proceedings of the 4th International Workshop on Information Retrieval with Asian Languages, IRAL’99, Taipei, Taiwan (pp. 55–62).
Chan, D. Y. M., & King, I. (1999b). Genetic algorithm for weights assignment in dissimilarity function for trademark retrieval. In Lecture Notes in Computer Science (Vol. 1614). Proceedings of the 3rd International Conference on Visual Information Systems, VISUAL’99, Amsterdam, The Netherlands (pp. 557–565). Berlin: Springer Verlag.
Corel Corporation. (2000). Corel Gallery 3.0. Available: http://www3.corel.com/.
Del Bimbo, A. (1999). Visual information retrieval. San Francisco: Morgan Kaufmann Publishers, Inc.
Garcia, J. A., Fdez-Valdivia, J., Fdez-Vidal, X. R., & Rodriguez-Sanchez, R. (2001). Information theoretic measure for visual target distinctness. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(4), 362–383.
Goldberg, D. E. (1989). Genetic algorithms in search, optimization and machine learning. Reading, MA: Addison- Wesley.
Laaksonen, J., Oja, E., Koskela, M., & Brandt, S. (2000). Analyzing low-level visual features using content-based image retrieval. In Proceedings of the 7th International Conference on Neural Information Processing (ICONIP’00), Taejon, Korea (pp. 1333–1338).
Lew, M. S. (Ed.). (2001). Principles of visual information retrieval. London: Springer Verlag.
Li, J., Wang, J. Z., & Wiederhold, G. (2000). IRM: Integrated region matching for image retrieval. In Proceedings of the 8th ACM Multimedia Conference, Los Angeles, CA, USA (pp. 147–156).
Massachusetts Institute of Technology, Media Lab. (2001). Vision Texture Database. Available: ftp://whitechapel. media.mit.edu/pub/VisTex/.
Rui, Y., & Huang, T. S. (2001). Relevance feedback techniques in image retrieval. In Lew (Ed.) (pp. 219–258) 2001 [Chap. 9].
Rui, Y., Huang, T. S., Ortega, M., & Mehrotra, S. (1998). Relevance feedback: a power tool for interactive contentbased image retrieval. IEEE Transactions on Circuits and Systems for Video Technology, 8(5), 644–655.
Santini, S., & Jain, R. (1997). Similarity is a geometer. Multimedia Tools and Applications, 5(3), 277–306. Santini, S., Gupta, A., & Jain, R. (2001). Emergent semantics through interaction in image databases. IEEE Transactions on Knowledge and Data Engineering, 13(3), 337–351.
Smeulders, A. W. M., Worring, M., Santini, S., Gupta, A., & Jain, R. (2000). Content-based image retrieval at the end of the early years. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(12), 1349–1380.
Steji�c, Z., Iyoda, E. M., Takama, Y., & Hirota, K. (2001). Automatic textual summarization of image database contents using combination of clustering and neural network techniques. In Proceedings of the 2nd International Conference on Intelligent Technologies, Intech 2001, Bangkok, Thailand (pp. 233–239).
Stricker, M., & Orengo, M. (1995). Similarity of color images. In Proceedings of IS&T and SPIEStorage and Retrieval of Image and Video Databases III, San Jose, CA, USA (pp. 381–392).
University of Southern California, Signal and Image Processing Institute. (2001). The USC-SIPI Image Database. Available: http://sipi.usc.edu/services/database/Database.html.
Vailaya, A., & Jain, A. K. (1998). Shape-based retrieval: a case study with trademark image databases. Pattern Recognition, 31(9), 1369–1390.
Vailaya, A., Jain, A. K., & Zhang, H. J. (1998). On image classification: city images vs. landscapes. Pattern Recognition, 31(12), 1921–1935.
Wandell, B. A. (1995). Foundations of vision. Sunderland, MA: Sinauer Associates.
Wang, J. Z., Li, J., & Wiederhold, G. (2001). SIMPLIcity: Semantics-sensitive Integrated Matching for Picture LIbraries. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(9), 947–963.
Zhou, X. S., & Huang, T. S. (2001). Exploring the nature and variants of relevance feedback. In Proceedings of IEEE Workshop on Content-Based Access of Image and Video Libraries, in conjunction with IEEE Conference on Computer Vision and Pattern Recognition (CVPR’01), Hawaii, USA.
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