Random projection tree and multiview embedding for large-scale image retrieval

Publication Type:
Conference Proceeding
Citation:
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2010, 6444 LNCS (PART 2), pp. 641 - 649
Issue Date:
2010-12-21
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Image retrieval on large-scale datasets is challenging. Current indexing schemes, such as k-d tree, suffer from the "curse of dimensionality". In addition, there is no principled approach to integrate various features that measure multiple views of images, such as color histogram and edge directional histogram. We propose a novel retrieval system that tackles these two problems simultaneously. First, we use random projection trees to index data whose complexity only depends on the low intrinsic dimension of a dataset. Second, we apply a probabilistic multiview embedding algorithm to unify different features. Experiments on MSRA large-scale dataset demonstrate the efficiency and effectiveness of the proposed approach. © 2010 Springer-Verlag.
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