Random Projection Tree and Multiview Embedding for Large-scale Image Retrieval

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Conference Proceeding
The 17th International Conference on Neural Information Processing: Models and Applications (ICONIP 2010), 2010, pp. 641 - 649
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Image retrieval on large-scale datasets is challenging. Current indexing schemes, such as k-d tree, suffer from the âcurse of dimensionalityâ . 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.
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