Abstract
Increasing of multimedia applications in commerce, biometrics, science, entertainments etc. leads to a great need of processing of digital visual content stored in very large databases. Many systems combine visual features and metadata analysis to solve the semantic gap between low-level visual features and high-level human concept, i.e. there arises a great interest in content-based image retrieval (CBIR) systems. As retrieval is computationally expensive, one of the most challenging moments in CBIR is minimizing of the retrieval process time. Widespread clustering techniques allow to group similar images in terms of their features proximity. The number of matches can be greatly reduced, but there is no guarantee that the global optimum solution is obtained. We propose a novel hierarchical clustering of image collections with objective function encompassing goals to number of matches at a search stage. Offered method enables construction of image retrieval systems with minimal query time.
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References
Chen, Y., Wang, J.Z.: Image categorization by learning and reasoning with regions. Journal of Machine Learning Research 5, 913–939 (2004)
Li, J., Wang, J.Z.: Automatic linguistic indexing of pictures by a statistical modeling approach. IEEE Transactions on Pattern Analysis and Machine Intelligence 25, 1075–1088 (2003)
Müller, H., Müller, W., Squire, D., Marchand-Maillet, S., Pun, T.: Performance Evaluation in Content-Based Image Retrieval: Overview and Proposals. Pattern Recognition Letters 22, 593–601 (2001)
Veltkamp, R., Burkhardt, H., Kriegel, H.-P.: State-of-the-Art in Content-Based Image and Video Retrieval. Kluwer Academic Publishers, Dordrecht (2001)
Greenspan, H., Dvir, G., Rubner, Y.: Context-Dependent Segmentation and Matching in Image Database. Computer Vision and Image understanding 93, 86–109 (2004)
Dimai, A.: Assessment of Effectiveness of Content-Based Image Retrieval Systems. In: Huijsmans, D.P., Smeulders, A.W.M. (eds.) VISUAL 1999. LNCS, vol. 1614, pp. 525–533. Springer, Heidelberg (1999)
Rui, Y., Huang, T., Chang, S.: Image Retrieval: Current Techniques, Promising Directions and Open Issues. Journal of Visual Communication and Image Representation 10, 39–62 (1999)
Yanai, K., Shindo, M., Noshita, K.: A Fast Image-Gathering System from the World-Wide Web Using a PC Cluster. Image and Vision Computing 22, 59–71 (2004)
Bauckhage, C., Braun, E., Sagerer, G.: From Image Features to Symbols and Vice Versa – Using Graphs to Loop Data – and Model-Driven Processing in Visual Assembly Recognition. International Journal of Pattern Recognition and Artificial Intelligence 18, 497–517 (2004)
Manjunath, B.S., Ma, W.Y.: Texture Features for Browsing and Retrieval of Large Image Data. IEEE Transactions on Pattern Analysis and Machine Intelligence 18, 837–842 (1996)
Celebi, E., Alpkocak, A.: Clustering of Texture Features for Content Based Image Retrieval. In: Yakhno, T. (ed.) ADVIS 2000. LNCS, vol. 1909, pp. 216–225. Springer, Heidelberg (2000)
Peng, J., Bhanu, B., Qing, S.: Probabilistic Feature Relevance Learning for Content-Based Image Retrieval. Computer Vision and Image Understanding 75, 150–164 (1999)
Cox, I.J., Miller, M.L., Minka, T.P., Papathomas, T., Yianilos, P.N.: The Bayesian Image Retrieval System, PicHunter: Theory, Implementation, and Psychophysical Experiments. IEEE Transactions on Image Processing 9, 20–37 (2000)
Carson, C., Belongie, S., Greenspan, H., Malik, J.: Region-Based Image Querying. In: Proceedings of the IEEE Workshop on Content-Based Access of Image and Video Libraries (CVPR 1997), pp. 42–49 (1997)
Tian, Q., Sebe, N., Lew, M.S., Loupias, E., Huang, T.S.: Image Retrieval Using Wavelet-Based Salient Points. Journal of Electronic Imaging 10, 835–849 (2001)
Cinque, L., De Rosa, F., Lecca, F., Levialdi, S.: Image Retrieval Using Resegmentation Driven by Query Rectangles. Image and Vision Computing 22, 15–22 (2004)
Santini, S., Gupta, A., Jain, R.: Emergent Semantics through Interaction in Image Databases. Knowledge and Data Engineering 13, 337–351 (2001)
Sheikholeslami, G., Chang, W., Zhang, A.: SemQuery: Semantic Clustering and Querying on Heterogeneous Features for Visual Data. IEEE Transactions on Knowledge and Data Engineering 14, 988–1002 (2002)
Käster, T., Wendt, V., Sagerer, G.: Comparing Clustering Methods for Database Categorization in Image Retrieval. In: Michaelis, B., Krell, G. (eds.) DAGM 2003. LNCS, vol. 2781, pp. 228–235. Springer, Heidelberg (2003)
Mashtalir, V.P., Yakovlev, S.V.: Point-Set Methods of Clusterization of Standard Information. In: Cybernetics and Systems Analysis, vol. 37, pp. 295–307. Kluwer Academic Publishers, Dordrecht (2001)
Kinoshenko, D., Mashtalir, V., Orlov, A., Yegorova, E.: Method of Creating of Functional Invariants under One-Parameter Geometric Image Transformations. In: Michaelis, B., Krell, G. (eds.) DAGM 2003. LNCS, vol. 2781, pp. 68–75. Springer, Heidelberg (2003)
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Kinoshenko, D., Mashtalir, V., Yegorova, E., Vinarsky, V. (2005). Hierarchical Partitions for Content Image Retrieval from Large-Scale Database. In: Perner, P., Imiya, A. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2005. Lecture Notes in Computer Science(), vol 3587. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11510888_44
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DOI: https://doi.org/10.1007/11510888_44
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26923-6
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