Abstract
Image similarity measures are at the core of every image retrieval system. In this contribution, we provide a systematic overview of distribution based measures for image similarity. We then empirically compare nine families of color and texture similarity measures summarizing over 1,000 CPU hours of computational experiments. Quantitative performance evaluations are given for classification and image retrieval.
Based on the empirical findings a novel image retrieval framework is developed relying on the following fundamental design decisions: First, database items are described by generative probabilistic models. Second, similarity between a query and a database image is measured in terms of how well the corresponding generative model describes or explains the new query. Besides its statistical foundation the proposed procedure has the following key advantages: (i) The probabilistic models can be estimated independently from each other. Thus no joint histogram binning for the complete database as in most commonly employed methods is necessary. (ii) It is possible to model different cues for different images. (iii) The approach can naturally be extended to more refined models.
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References
J. R. Bach, C. Fuller, A. Gupta, A. Hampapur, B. Horowitz, R. Humphrey, R. Jain, and C. Shu. Virage image search engine: an open framework for image management. In SPIE Conference on Storage and Retrieval for Image and Video Databases IV, volume 2670, pages 76-87, March 1996.
S. Belongie, C. Carson, H. Greenspan, and J. Malik. Color-and texture—based image segmentation using expectation—maximization algorithm and its application to content—based image retrieval. In Proc. International Conference on Computer Vision (ICCV’98), pages 675-682, 1998.
K. Clarkson. Nearest neighbor queries in metric spaces. In ACM Symposium on the Theory of Computing, pages 609-617, 1997.
T. Cover and J. Thomas. Elements of Information Theory. Wiley and Sons, 1991.
G. Cross and A. Jain. Markov random field texture models. IEEE PAMI, 5: 25 - 39, 1983.
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, pages 23 - 32, September 1995.
D. Forsyth, J. Malik, M. Fleck, H. Greenspan, and T. Leung. Finding pictures of objects in large collections of images. In International Workshop on Object Recognition for Computer Vision, Cambridge, UK, April 1996.
D. Geman, S. Geman, C. Graffigne, and P. Dong. Boundary detection by constrained optimization. IEEE PAMI, 12 (7): 609 - 628, 1990.
G. Gimel’farb and A. Jain. On retrieving textured images from image database. Pattern Recognition, 29 (9): 1461 - 1483, 1996.
J. Hafner, H. Sawhney, W. Equitz, M. Flickner, and W. Niblack. Efficient color histogram indexing for quadratic form distance functions. IEEE PAMI, 17 (7): 729 - 736, 1995.
R. Haralick, K. Shanmugan, and I. Dinstein. Textural features for image classification. IEEE Transactions on Systems, Man and Cybernetics, 3 (1): 610 - 621, 1973.
T. Hofmann, J. Puzicha, and J. Buhmann. Unsupervised texture segmentation in a deterministic annealing framework. IEEE PAMI, 20 (8): 803 - 818, 1998.
A. Jain and F. Farrokhnia. Unsupervised texture segmentation using Gabor filters. Pattern Recognition, 24 (12): 1167 - 1186, 1991.
A. Laine and J. Fan. Texture classification by wavelet packet signatures. IEEE PAMI, 15: 1186 - 1191, 1993.
J. Malik, S. Belongie, J. Shi, and T. Leung. Textons, contours and regions: Cue integration in image segmentation. In Proceedings of the International Conference on Computer Vision (ICCV’99), pages 918-925, 1999.
B. Manjunath and W. Ma. Texture features for browsing and retrieval of image data. IEEE PAMI, 18 (8): 837 - 842, 1996.
J. Mao and A. Jain. Texture classification and segmentation using multiresolution simultaneous autoregressive models. Pattern Recognition, 25 (2): 173 - 188, 1992.
T. Ojala, M. Pietikäinen, and D. Harwood. A comparative study of texture measures with classification based feature distributions. Pattern Recognition, 29 (1): 51 - 59, 1996.
A. Pentland, R. W. Picard, and S. Sclaroff. Photobook: content-based manipulation of image databases. Int. J. Computer Vision, 18 (3): 233 - 254, 1996.
R. W. Picard and T. P. Minka. Vision texture for annotation. Multimedia Systems, 3: 3 - 14, 1995.
O. Pichler, A. Teuner, and B. Hosticka. A comparison of texture feature extraction using adaptive Gabor filtering, pyramidal and tree—structured wavelet transforms. Pattern Recognition, 29 (5): 733 - 742, 1996.
J. Puzicha, T. Hofmann, and J. Buhmann. Non—parametric similarity measures for unsupervised texture segmentation and image retrieval. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR’97), pages 267-272, 1997.
J. Puzicha, T. Hofmann, and J. Buhmann. Histogram clustering for unsupervised segmentation and image retrieval. Pattern Recognition Letters, 20 (9): 899 - 909, 1999.
J. Puzicha, Y. Rubner, C. Tomasi, and J. Buhmann. Empirical evaluation of dissimilarity measures for color and texture. In Proceedings of the International Conference on Computer Vision (ICCV’99), pages 11651173, 1999.
Y. Rubner, J. Puzicha, C. Tomasi, and J. Buhmann. Empirical evaluation of dissimilarity measures for color and texture (to appear). Computer Vision and Image Understanding, 2001.
Y. Rubner, C. Tomasi, and L. J. Guibas. A metric for distributions with applications to image databases. In IEEE International Conference on Computer Vision, pages 59 - 66, Bombay, India, January 1998.
M. Ruzon and C. Tomasi. Color edge detection with the compass operator. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR ’99), pages 160-166, 1999.
M. Swain and D. Ballard. Color indexing. International Journal of Computer Vision, 7(1): 11 - 32, 1991.
N. Vasconcelos and A. Lippman. Embedded mixture modeling for efficient probabilistic content-based indexing and retrieval. In Proceedings of the SPIE Conference on Multimedia Storage and Archiving Systems III, pages 134-143, 1998.
N. Vasconcelos and A. Lippman. Probabilistic retrieval: new insights and experimental results. In Proceedings IEEE Workshop on Content-Based Access of Image and Video Libraries, pages 62-66, 1999.
H. Voorhees and T. Poggio. Computing texture boundaries from images. Nature, 333: 364 - 367, 1988.
G. Wyszecki and W. S. Stiles. Color Science: Concepts and Methods, Quantitative Data and Formulae. John Wiley and Sons, New York, NY, 1982.
S.C. Zhu. Embedding Gestalt laws in the Markov random fields - a theory for shape modeling and perceptual organization. IEEE PAMI, 21 (11): 1170 - 1187, 1999.
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Puzicha, J. (2001). Distribution-Based Image Similarity. In: Veltkamp, R.C., Burkhardt, H., Kriegel, HP. (eds) State-of-the-Art in Content-Based Image and Video Retrieval. Computational Imaging and Vision, vol 22. Springer, Dordrecht. https://doi.org/10.1007/978-94-015-9664-0_7
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DOI: https://doi.org/10.1007/978-94-015-9664-0_7
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