Abstract:
Image retrieval on large-scale datasets is challenging. Current indexing schemes, such as k-d tree, suffer from the â¿¿curse 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.
