Nearest Neighbour Search Using Binary Neural Networks
2016 International Joint Conference on Neural Networks (IJCNN)(2016)
摘要
The problem of finding nearest neighbours in terms of Euclidean distance, Hamming distance or other distance metric is a very common operation in computer vision and pattern recognition. In order to accelerate the search for the nearest neighbour in large collection datasets, many methods rely on the coarse-fine approach.In this paper we propose to combine Product Quantization (PQ) and binary neural associative memories to perform the coarse search. Our motivation lies in the fact that neural network dimensions of the representation associated with a set of k vectors is independent of k. We run experiments on TEXMEX SIFT1M and MNIST databases and observe significant improvements in terms of complexity of the search compared to raw PQ.
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关键词
nearest neighbour search,binary neural networks,product quantization,binary neural associative memories,TEXMEX SIFT1M database,MNIST database,search complexity
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