Searching for nearest neighbors with a dense space partitioning

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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.

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