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Product sub-vector quantization for feature indexing

The-Anh Pham, Dinh-Nghiep Le, Thi-Lan-Phuong Nguyen


This work addresses the problem of feature indexing to significantly accelerate the matching process which is commonly known as a cumbersome task in many computer vision applications. To this aim, we propose to perform product sub-vector quantization (PSVQ) to create finer representation of  underlying data while still maintaining reasonable memory allocation. In addition, the quantized data can be  jointly used with a clustering tree to perform approximate nearest search very efficiently. Experimental results demonstrate the superiority of the proposed method for different datasets in comparison with other methods.


Product quantization; Hierarchical clustering tree; Approximate nearest search

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Journal of Computer Science and Cybernetics ISSN: 1813-9663

Published by Vietnam Academy of Science and Technology