Abstract
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.
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Xie, B., Mu, Y., Song, M., Tao, D. (2010). Random Projection Tree and Multiview Embedding for Large-Scale Image Retrieval. In: Wong, K.W., Mendis, B.S.U., Bouzerdoum, A. (eds) Neural Information Processing. Models and Applications. ICONIP 2010. Lecture Notes in Computer Science, vol 6444. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17534-3_79
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DOI: https://doi.org/10.1007/978-3-642-17534-3_79
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