Abstract
Target search in content-based image retrieval (CBIR) systems refers to finding a specific (target) image such as a particular registered logo or a specific historical photograph. Existing techniques were designed around query refinement based on relevance feedback, suffer from slow convergence, and do not even guarantee to find intended targets. To address those limitations, we propose several efficient query point movement methods. We theoretically prove that our approach is able to reach any given target image with fewer iterations in the worst and average cases. Extensive experiments in simulated and realistic environments show that our approach significantly reduces the number of iterations and improves overall retrieval performance. The experiments also confirm that our approach can always retrieve intended targets even with poor selection of initial query points and can be employed to improve the effectiveness and efficiency of existing CBIR systems.
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References
Browne, P., Smeaton, A.F.: Video Information Retrieval Using Objects andOstensive Relevance Feedback. In: Proceedings of the ACM Symposium on Applied Computing (SAC), pp. 1084–1090 (2004)
Carson, C., Belongie, S., Greenspan, H., Malik, J.: Blobworld: image segmentation using expectation-maximization and its application to image querying. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(8), 1026–1038 (2002)
Chakrabarti, K., Michael, O.-B., Mehrotra, S., Porkaew, K.: Evaluating refined queries in top-k retrieval systems. IEEE Transactions on Knowledge and Data Engineering 16(2), 256–270 (2004)
Cox, I.J., Miller, M.L., Minka, T.P., Papathomas, T.V., Yianilos, P.N.: The Bayesian image retrieval system, PicHunter: theory, implementation, and psychophysical experiments. IEEE Transactions on Image Processing 9(1), 20–37 (2000)
Flickner, M., Sawhney, H.S., Ashley, J., Huang, Q., Dom, B., Gorkani, M., Hafner, J., Lee, D., Petkovic, D., Steele, D., Yanker, P.: Query by image and video content: The QBIC system. IEEE Computer 28(9), 23–32 (1995)
Gevers, T., Smeulders, A.: Content-based image retrieval: An overview. In: Medioni, G., Kang, S.B. (eds.) Emerging Topics in Computer Vision, Prentice Hall, Englewood Cliffs (2004)
Hua, K.A., Yu, N., Liu, D.: Query Decomposition: A Multiple Neighborhood Approach to Relevance Feedback Processing in Content-based Image Retrieval. In: Proceedings of the IEEE ICDE Conference (2006)
Ishikawa, Y., Subramanya, R., Faloutsos, C.: MindReader: Querying databases through multiple examples. In: Proceedings of the 24th VLDB Conference, pp. 218–227 (1998)
Kim, D.-H., Chung, C.-W.: Qcluster: relevance feedback using adaptive clustering for content-based image retrieval. In: Proceedings of the ACM SIGMOD Conference, pp. 599–610 (2003)
Liu, D., Hua, K.A., Vu, K., Yu, N.: Efficient Target Search with Relevance Feedback for Large CBIR Systems. In: Proceedings of the 21st Annual ACM Symposium on Applied Computing (2006)
Michael, O.-B., Mehrotra, S.: Relevance feedback techniques in the MARS image retrieval systems. Multimedia Systems (9), 535–547 (2004)
Nakazato, M., Manola, L., Huang, T.S.: ImageGrouper: a group-oriented user interface for content-based image retrieval and digital image arrangement. Journal of Visual Languages and Computing 14(4), 363–386 (2003)
Preparata, F.P., Shamos, M.I.: Computational Geometry: An Introduction. Springer, New York (1985)
Rui, Y., Huang, T., Ortega, M., Mehrotra, S.: Relevance feedback: A power tool for interactive content-based image retrieval. IEEE Transactions on Circuits and Systems for Video Technology 8(5), 644–655 (1998)
Shen, H.T., Ooi, B.C., Zhou, X.: Towards effective indexing for very large video sequence database. In: Proceedings of the ACM SIGMOD Conference, pp. 730–741 (2005)
Smeulders, A.W.M., Worring, M., Santini, A.G.S., Jain, R.: Content-based image retrieval at the end of the early years. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(12), 1349–1380 (2000)
Smith, J.R., Chang, S.-F.: Transform features for texture classification and discrimination in large image databases. In: Proceedings of the International Conference on Image Processing, pp. 407–411 (1994)
Smith, J.R., Chang, S.-F.: VisualSEEk: A fully automated content-based image query system. In: Proceedings of the 4th ACM Multimedia Conference, pp. 87–98 (1996)
Stricker, M.A., Orengo, M.: Similarity of color images. In: Proceedings of Storage and Retrieval for Image and Video Databases (SPIE), pp. 381–392 (1995)
Vu, K., Hua, K.A., Tavanapong, W.: Image retrieval based on regions of interest. IEEE Transactions on Knowledge and Data Engineering 15(4), 1045–1049 (2003)
Wang, J.Z., Li, J., Wiederhold, G.: SIMPLIcity: Semantics-sensitive integrated matching for picture libraries. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(9), 947–963 (2001)
Wu, L., Faloutsos, C., Sycara, K., Payne, T.R.: FALCON: feedback adaptive loop for content-based retrieval. In: Proceedings of the 26th VLDB Conference, pp. 297–306 (2000)
Zhou, X.S., Huang, T.S.: Edge-based structural features for content-based image retrieval. Pattern Recognition Letters 22(5), 457–468 (2001)
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Liu, D., Hua, K.A., Vu, K., Yu, N. (2006). Fast Query Point Movement Techniques with Relevance Feedback for Content-Based Image Retrieval. In: Ioannidis, Y., et al. Advances in Database Technology - EDBT 2006. EDBT 2006. Lecture Notes in Computer Science, vol 3896. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11687238_42
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DOI: https://doi.org/10.1007/11687238_42
Publisher Name: Springer, Berlin, Heidelberg
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