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Searching for nearest neighbors with a dense space partitioning
Description
Product quantization based approximate nearest neighbor search with the use of inverted index structures have recently received increasing attention. In this paper, we propose a new inverted index structure for searching nearest neighbors in very large datasets of high dimensional data. For data indexing, our proposed method creates a dense space partitioning using multiple centroids based assigning, which generates shorter candidate lists and improves the search speed. Our experiments with a dataset of one billion SIFT features show that while achieving higher accuracy, our method demonstrates better performances on search speed compared to IV-FADC, the conventional product quantization based inverted index structure.
Journal
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- 2015 IEEE International Conference on Image Processing (ICIP)
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2015 IEEE International Conference on Image Processing (ICIP) 4461-4465, 2015-09-01
IEEE