Product sub-vector quantization for feature indexing
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DOI:
https://doi.org/10.15625/1813-9663/35/1/13442Keywords:
Product quantization, Hierarchical clustering tree, Approximate nearest searchAbstract
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.Downloads
Published
18-03-2019
How to Cite
[1]T.-A. Pham, D.-N. Le, and T.-L.-P. Nguyen, “Product sub-vector quantization for feature indexing”, J. Comput. Sci. Cybern., vol. 35, no. 1, p. 69–83, Mar. 2019.
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Section
Computer Science
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