Open Access Open Access  Restricted Access Subscription Access

Product sub-vector quantization for feature indexing

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

Abstract


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.

Keywords


Product quantization; Hierarchical clustering tree; Approximate nearest search

Full Text:

PDF


DOI: https://doi.org/10.15625/1813-9663/35/1/13442 Display counter: Abstract : 152 views. PDF : 69 views.

Journal of Computer Science and Cybernetics ISSN: 1813-9663

Published by Vietnam Academy of Science and Technology