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Erschienen in:
2016 | OriginalPaper | Buchkapitel
Fast Nearest Neighbor Search in the Hamming Space
verfasst von : Zhansheng Jiang, Lingxi Xie, Xiaotie Deng, Weiwei Xu, Jingdong Wang
Erschienen in: MultiMedia Modeling
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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.