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Learning Optimal Representations for Image Retrieval Applications

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Image and Video Retrieval (CIVR 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2728))

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Abstract

This paper presents an MCMC stochastic gradient algorithm for finding representations with optimal retrieval performance on given image datasets. For linear subspaces in the image space and the spectral space, the problem is formulated as that of optimization on a Grassmann manifold. By exploiting the underlying geometry of the manifold, a computationally effective algorithm is developed. The feasibility and effectiveness of the proposed algorithm are demonstrated through extensive experimental results.

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© 2003 Springer-Verlag Berlin Heidelberg

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Liu, X., Srivastava, A., Sun, D. (2003). Learning Optimal Representations for Image Retrieval Applications. In: Bakker, E.M., Lew, M.S., Huang, T.S., Sebe, N., Zhou, X.S. (eds) Image and Video Retrieval. CIVR 2003. Lecture Notes in Computer Science, vol 2728. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45113-7_6

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  • DOI: https://doi.org/10.1007/3-540-45113-7_6

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40634-1

  • Online ISBN: 978-3-540-45113-6

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