Query-driven iterated neighborhood graph search for large scale indexing
Proceedings of the 20th ACM international conference on Multimedia
Similar image search with a tiny bag-of-delegates representation
Proceedings of the 20th ACM international conference on Multimedia
Scalable similar image search by joint indices
Proceedings of the 20th ACM international conference on Multimedia
Efficient mining of repetitions in large-scale TV streams with product quantization hashing
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part I
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Proceedings of the 21st ACM international conference on Multimedia
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The k-NN graph has played a central role in increasingly popular data-driven techniques for various learning and vision tasks; yet, finding an efficient and effective way to construct k-NN graphs remains a challenge, especially for large-scale high-dimensional data. In this paper, we propose a new approach to construct approximate k-NN graphs with emphasis in: efficiency and accuracy. We hierarchically and randomly divide the data points into subsets and build an exact neighborhood graph over each subset, achieving a base approximate neighborhood graph; we then repeat this process for several times to generate multiple neighborhood graphs, which are combined to yield a more accurate approximate neighborhood graph. Furthermore, we propose a neighborhood propagation scheme to further enhance the accuracy. We show both theoretical and empirical accuracy and efficiency of our approach to k-NN graph construction and demonstrate significant speed-up in dealing with large scale visual data.