Multidimensional binary search trees used for associative searching
Communications of the ACM
ImageMap: An Image Indexing Method Based on Spatial Similarity
IEEE Transactions on Knowledge and Data Engineering
WALRUS: A Similarity Retrieval Algorithm for Image Databases
IEEE Transactions on Knowledge and Data Engineering
Random projection trees and low dimensional manifolds
STOC '08 Proceedings of the fortieth annual ACM symposium on Theory of computing
MSRA-MM 2.0: A Large-Scale Web Multimedia Dataset
ICDMW '09 Proceedings of the 2009 IEEE International Conference on Data Mining Workshops
Biased discriminant euclidean embedding for content-based image retrieval
IEEE Transactions on Image Processing
Active reranking for web image search
IEEE Transactions on Image Processing
Biologically inspired feature manifold for scene classification
IEEE Transactions on Image Processing
Bregman Divergence-Based Regularization for Transfer Subspace Learning
IEEE Transactions on Knowledge and Data Engineering
Manifold elastic net: a unified framework for sparse dimension reduction
Data Mining and Knowledge Discovery
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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.