Browse > Article

A Similarity Ranking Algorithm for Image Databases  

Cha, Guang-Ho (서울산업대학교 컴퓨터공학과)
Publication Information
Journal of KIISE:Databases / v.36, no.5, 2009 , pp. 366-373 More about this Journal
Abstract
In this paper, we propose a similarity search algorithm for image databases. One of the central problems regarding content-based image retrieval (CBIR) is the semantic gap between the low-level features computed automatically from images and the human interpretation of image content. Many search algorithms used in CBIR have used the Minkowski metric (or $L_p$-norm) 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. Our new search algorithm tackles this problem by employing new similarity measures and ranking strategies that reflect the nonlinearity of human perception and contextual information. Our search algorithm yields superior experimental results on a real handwritten digit image database and demonstrates its effectiveness.
Keywords
image database; content-based retrieval; similarity search; similarity ranking;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Tong, S. and Chang, E., 'Support Vector Machine Active Learning for Image Retrieval,' Proc. ACM Multimedia, pp.107-118, 2001   DOI
2 Scholkopf, B. et al., 'Comparing Support Vector Machines with Gaussian Kernels to Radial Basis Function Classifiers,' IEEE Trans. Signal Processing, vol.45, pp.2758-2765, 1997   DOI   ScienceOn
3 He, X., Ma, W.-Y, and Zhang, H.- J. 'Learning an image manifold for retrieval,' Proc. ACM Multimedia, pp.17-23, 2004   DOI
4 Wu, G., Chang, KY., and Panda, N., 'Formulating Context-dependent Similarity Functions,' Proc. ACM Multimedia, pp.725-734, 2005   DOI
5 Goh, K-S., Li, B., and Chang, E., 'DynDex: A Dynamic and Non-metric Space Indexer,' Proc. ACM Multimedia, pp.466-475, 2002   DOI
6 Valois, RL.De and Valois, K.K.De, Spatial Vision, Oxford Science Pub., Oxford, 1988
7 Shi, J and Malik, J., 'Normalized Cuts and Image Segmentation,' IEEE Trans. PAMI, vol.22, no.8, pp.888-905, 2000   DOI   ScienceOn
8 LeCun, Y et al, 'Gradient-based learning applied to document recognition,' Proceedings of the IEEE, vol.86, no.11, pp.2278-2324, 1998   DOI   ScienceOn
9 Ng, AY., Jordan, M.L, and Weiss, Y, 'On Spectral Clustering: Analysis and Algorithm,' Advances in Neural Information Processing Systems, 14, MIT Press, MA, 2001
10 Haykin, S., Neural Networks: A Comprehensive Foundation, Maxmillan, NY, 1994
11 Ishikawa, Y, Subramanya, R, and Faloutsos, C., 'Mindfieader: Querying databases through multiple examples,' Proc. VLDB, pp.218-227, 1998
12 Rui, Y, et aI., 'Relevance feedback: A Power tool for interactive content-based image retrieval,' IEEE Trans. Circuits and Video Technology, vol.8, no.5, pp.644-644, 1998   DOI   ScienceOn
13 Hoi, C.-H. and Lyu, M., 'A novel log-based relevance feedback technique in content-based image retrieval,' Proc. ACM Multimedia, pp.24-31, 2004   DOI
14 Scholkopf, B., Smola, A., and MOller, K, 'Nonlinear Component Analysis as a Kernel Eigenvalue Problem,' Neural Computation, vol.10, pp.1299-1319, 1998   DOI   ScienceOn
15 Wu, L., Faloutsos, C., Sycara, K, and Payne, T.R., 'FALCON: Feedback Adaptive Loop for Content-Based Retrieval,' Proc. VLDB, pp.297-306, 2000
16 Rui, Y, Huang, T.S., and Mehrotra, S., 'Contentbased image retrieval with relevance feedback in MARS,' Proc. ICIP, pp.815-818, 1997   DOI
17 Vapnik, V.N., Statistical Learning Theory, Wiley, NY, 1998
18 Zhou, D. et al., 'Learning with Local and Global Consistency,' Advances in Neural Information Processing Systems, 16, MIT Press, Cambridge, MA,2004