A Context-Sensitive Algorithm for Media Similarity Search

Authors: Guang-Ho Cha

Abstract:

This paper presents a context-sensitive media similarity search algorithm. One of the central problems regarding media search is the semantic gap between the low-level features computed automatically from media data and the human interpretation of them. This is because the notion of similarity is usually based on high-level abstraction but the low-level features do not sometimes reflect the human perception. Many media search algorithms have used the Minkowski metric to measure similarity between image pairs. However those functions cannot adequately capture the aspects of the characteristics of the human visual system as well as the nonlinear relationships in contextual information given by images in a collection. Our search algorithm tackles this problem by employing a similarity measure and a ranking strategy that reflect the nonlinearity of human perception and contextual information in a dataset. Similarity search in an image database based on this contextual information shows encouraging experimental results.

Keywords: Context-sensitive search, image search, media search, similarity ranking, similarity search.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.2363157

References:

[1] Y. Ishikawa, R. Subramanya, and C. Faloutsos, “MindReader: Querying databases through multiple examples,” Proc. VLDB Conf., pp. 218-227, 1998
[2] Y. Rui, T.S. Huang, M. Ortega, and Mehrotra, S. “Relevance feedback: A Power tool for interactive content-based image retrieval,” IEEE Trans. Circuits and Video Technology, 8(5), 644-644, 1998
[3] Y. Rui, T.S. Huang, and S. Mehrotra, “Content-based image retrieval with relevance feedback in MARS,” Proc. of Int’l Conf. on Image Processing, 815-818, 1997
[4] S. Tong and E. Chang, “Support Vector Machine Active Learning for Image Retrieval,” Proc. ACM Multimedia Conf., pp. 107-118, 2001
[5] L. Wu, C. Faloutsos, K. Sycara, and T.R. Payne, “FALCON: Feedback Adaptive Loop for Content-Based Retrieval,” Proc. of VLDB Conf., pp. 297-306, 2000
[6] D. Zhou, O. Bousquet, T.N. Lal, J. Weston, and B. Schölkopf, “Learning with Local and Global Consistency,” Advances in Neural Information Processing Systems, 16, 2004, MIT Press, Cambridge, MA.
[7] G. Wu, E.Y. Chang, and N. Panda, “Formulating Context-dependent Similarity Functions,” Proc. ACM Multimedia, pp. 725-734, 2005
[8] S. Haykin, Neural Networks: A Comprehensive Foundation, 1994, Maxmillan, NY.
[9] B. Schölkopf, S. Kung, C. Burges, F. Girosi, P. Niyogi, T. Poggio, and V. Vapnik, “Comparing Support Vector Machines with Gaussian Kernels to Radial Basis Function Classifiers,” IEEE Trans. on Signal Processing, 45, 2758-2765, 1997
[10] V.N. Vapnik, Statistical Learning Theory, 1998, Wiley, NY.
[11] C.-H. Hoi and M. Lyu, “A novel log-based relevance feedback technique in content-based image retrieval,” Proc. ACM Multimedia Conf., pp. 24-31, 2004
[12] K. Barnard and D. Forsyth, “Learning the semantics of words and pictures,” Journal of Machine Learning Research, Vol. 3, pp. 1107-1135, 2003
[13] J. Jeon, V. Lavrenko, and R. Manmatha, “Automatic Image Annotation and Retrieval Using Cross-Media Relevance Models,” Proc. of ACM SIGIR Conf., 119-126, 2003
[14] J.Y. Pan, H.J, Yang, P. Duygulu, and C. Faloutsos, “Automatic Image Captioning,” Proc. IEEE Int’l Conf. on Multimedia and Expo, 1987-1990, 2004
[15] M. Srikanth, J. Varner, M. Bowden, and D. Moldovan, “Exploiting Ontologies for Automatic Image Annotation,” Proc. ACM SIGIR Conf., 552-558, 2005
[16] X. He, W.-Y. Ma, and H.-J. Zhang, “Learning an image manifold for retrieval,” Proc. ACM Multimedia Conf., pp. 17-23, 2004
[17] K.-S. Goh, B. Li, and E. Chang, “DynDex: A Dynamic and Non-metric Space Indexer,” Proc. ACM Multimedia, pp. 466-475, 2002
[18] R.L.De. Valois and K.K.De. Valois, Spatial Vision, 1988, Oxford Science Publications, Oxford
[19] P. Muneesawang and L. Guan, “An Interactive Approach for CBIR Using a Network of Radial Basis Functions,” IEEE Trans. on Multimedia, 6(5), 703-716, 2004
[20] D. Ennis, “Modeling similarity and identification when there are momentary fluctuations in psychological amplitudes,” Multidimensional Models of Perception and Cognition, 279-298, 1998, Lawrence Erlbaum Assoc. Pub., Philadelphia, PA.
[21] R. Shepard, “Toward a universal law of generalization for psychological science,” Science, 237, 1317-1323, 1987
[22] J. Shi and J. Malik, “Normalized Cuts and Image Segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(8), 888-905, 2000
[23] G. Chen et al., “HISA: A Query System Bridging the Semantic Gap for Large Image Databases,” Proc. of VLDB Conf. 2006, pp. 1187-1190.
[24] Y. LeCun, C. Cortes, and C.J.C. Burges, The MNIST Database of Handwritten Digits, http://yann.lecun.com/exdb/mnist/