Abstract
Recent years have witnessed growing interests in computing compact binary codes and binary visual descriptors to alleviate the heavy computational costs in large-scale visual research. However, it is still computationally expensive to linearly scan the large-scale databases for nearest neighbor (NN) search. In [15], a new approximate NN search algorithm is presented. With the concept of bridge vectors which correspond to the cluster centers in Product Quantization [10] and the augmented neighborhood graph, it is possible to adopt an extract-on-demand strategy on the online querying stage to search with priority. This paper generalizes the algorithm to the Hamming space with an alternative version of k-means clustering. Despite the simplicity, our approach achieves competitive performance compared to the state-of-the-art methods, i.e., MIH and FLANN, in the aspects of search precision, accessed data volume and average querying time.
This work was done when Zhansheng Jiang was an intern at Microsoft Research, P.R. China.
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Weiwei Xu is partially supported by NSFC 61322204.
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Jiang, Z., Xie, L., Deng, X., Xu, W., Wang, J. (2016). Fast Nearest Neighbor Search in the Hamming Space. In: Tian, Q., Sebe, N., Qi, GJ., Huet, B., Hong, R., Liu, X. (eds) MultiMedia Modeling. MMM 2016. Lecture Notes in Computer Science(), vol 9516. Springer, Cham. https://doi.org/10.1007/978-3-319-27671-7_27
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DOI: https://doi.org/10.1007/978-3-319-27671-7_27
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