Abstract
Content-Based Image Retrieval is a challenging problem both in terms of effectiveness and efficiency. In this paper, we present a flexible cluster-and-search approach that is able to reuse any previously proposed image descriptor as long as a suitable similarity function is provided. In the clustering step, the image data set is clustered using a hybrid divisive-agglomerative hierarchical clustering technique. The obtained clusters are organized in a tree that can be traversed efficiently using the similarity function associated with the chosen image descriptors. Our experiments have shown that we can improve search-time performance by a factor of 10 or more, at the cost of small loss in effectiveness (typically less than 15%) when compared to the state-of-the-art solutions.
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References
Antani, S., Long, R., Thoma, G.: Content-based image retrieval for large biomedical image archives. In: MEDINFO (2004)
Bhatia, S.: Hierarchical clustering for image databases. In: Intl. Conference on Electro Information Technology, pp. 6–12 (2005)
Bimbo, A.D.: Visual Information Retrieval, 1st edn. Morgan Kaufmann, San Francisco (1999)
Bishop, C.: Pattern Recognition and Machine Learning, 1st edn. Springer, Heidelberg (2006)
Thies, C., Malik, A., Keysers, D., et al.: Hierarchical feature clustering for CBIR in medical image databases. In: Medical Imaging, pp. 598–608 (2003)
Han, J., Kamber, M.: Data Mining: Concepts and Techniques, 1st edn. Morgan Kaufmann, San Francisco (2005)
Kinoshenko, D., Mashtalir, V., Yegorova, E.: Hierarchical Partitions for Content Image Retrieval from Large-Scale Database. In: Perner, P., Imiya, A. (eds.) MLDM 2005. LNCS (LNAI), vol. 3587, pp. 445–455. Springer, Heidelberg (2005)
Shyu, M.-L., Chen, S.-C., Chen, M., et al.: A unified framework for image database clustering and CBIR. In: MMDBS, pp. 19–27 (2004)
Pass, G., Zabih, R., Miller, J.: Comparing images using color coherence vectors. In: ACMMM (1997)
Seo, J., Shneiderman, B.: Interactive Exploration of Multidimensional Microarray Data: Scatterplot Ordering, Gene Ontology Browser, and Profile Search. Phd thesis, University of Maryland, College Park (2003)
Stehling, R., Nascimento, M., Falcão, A.: An adaptive and efficient clustering-based approach for CBIR in image databases. In: IDEAS, pp. 356–365 (2001)
Stehling, R., Nascimento, M., Falcão, A.: A compact and efficient image retrieval approach based on border/interior classification. In: CIKM, pp. 102–109 (2002)
Swain, M.J., Ballard, D.H.: Color indexing. IJCV 7(1), 11–32 (1991)
Wu, W., Xiong, H., Shekhar, S. (eds.): Clustering and Information Retrieval. Kluwer, Dordrecht (2003)
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Rocha, A., Almeida, J., Nascimento, M.A., Torres, R., Goldenstein, S. (2008). Efficient and Flexible Cluster-and-Search for CBIR. In: Blanc-Talon, J., Bourennane, S., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2008. Lecture Notes in Computer Science, vol 5259. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88458-3_8
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DOI: https://doi.org/10.1007/978-3-540-88458-3_8
Publisher Name: Springer, Berlin, Heidelberg
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