Abstract
Distance metric learning has been widely investigated in machine learning and information retrieval. In this paper, we study a particular content-based image retrieval application of learning distance metrics from historical relevance feedback log data, which leads to a novel scenario called collaborative image retrieval. The log data provide the side information expressed as relevance judgements between image pairs. Exploiting the side information as well as inherent neighborhood structures among examples, we design a convex regularizer upon which a novel distance metric learning approach, named output regularized metric learning, is presented to tackle collaborative image retrieval. Different from previous distance metric methods, the proposed technique integrates synergistic information from both log data and unlabeled data through a regularization framework and pilots the desired metric toward the ideal output that satisfies pairwise constraints revealed by side information. The experiments on image retrieval tasks have been performed to validate the feasibility of the proposed distance metric technique.
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Hoi, S.C., Lyu, M.R., Jin, R.: A unified log-based relevance feedback scheme for image retrieval. IEEE Trans. Knowledge and Data Engineering 18(4), 509–524 (2006)
Fukunaga, K.: Introduction to Statistical Pattern Recognition. Elsevier, Amsterdam (1990)
Goldberger, G.H.J., Roweis, S., Salakhutdinov, R.: Neighbourhood components analysis. In: NIPS 17 (2005)
Globerson, A., Roweis, S.: Metric learning by collapsing classes. In: NIPS 18 (2006)
Weinberger, K., Blitzer, J., Saul, L.: Distance metric learning for large margin nearest neighbor classification. In: NIPS 18 (2006)
Yang, L., Jin, R., Sukthankar, R., Liu, Y.: An efficient algorithm for local distance metric learning. In: Proc. AAAI (2006)
Xing, E.P., Ng, A.Y., Jordan, M.I., Russell, S.: Distance metric learning with application to clustering with side-information. In: NIPS 15 (2003)
Bar-Hillel, A., Hertz, T., Shental, N., Weinshall, D.: Learning a mahalanobis metric from equivalence constraints. JMLR 6, 937–965 (2005)
Hoi, S.C., Liu, W., Lyu, M.R., Ma, W.-Y.: Learning distance metrics with contextual constraints for image retrieval. In: Proc. CVPR (2006)
Si, L., Jin, R., Hoi, S.C., Lyu, M.R.: Collaborative image retrieval via regularized metric learning. ACM Multimedia Systems Journal 12(1), 34–44 (2006)
Smeulders, A.W.M., Worring, M., Santini, S., Gupta, A., Jain, R.: Content-based image retrieval at the end of the early years. IEEE Trans. PAMI 22(12), 1349–1380 (2000)
Vapnik, V.N.: Statistical Learning Theory. John Wiley and Sons, Chichester (1998)
Goldberg, A., Zhu, X., Wright, S.: Dissimilarity in graph-based semi-supervised classification. In: Proc. Artificial Intelligence and Statistics (2007)
Boyd, S., Vandenberge, L.: Convex Optimization. Cambridge University Press, Cambridge (2003)
Manjunath, B., Newsam, P.W.S., Shin, H.: A texture descriptor for browsing and similarity retrieval. Signal Processing Image Communication (2001)
Davis, J.V., Kulis, B., Jain, P., Sra, S., Dhillon, I.S.: Information-theoretic metric learning. In: Proc. ICML (2007)
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Liu, W., Hoi, S.C.H., Liu, J. (2008). Output Regularized Metric Learning with Side Information. In: Forsyth, D., Torr, P., Zisserman, A. (eds) Computer Vision – ECCV 2008. ECCV 2008. Lecture Notes in Computer Science, vol 5304. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88690-7_27
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DOI: https://doi.org/10.1007/978-3-540-88690-7_27
Publisher Name: Springer, Berlin, Heidelberg
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