Please use this identifier to cite or link to this item: http://dspace.ctu.edu.vn/jspui/handle/123456789/12147
Title: Product sub-vector quantization for feature indexing
Authors: Pham, The Anh
Le, Dinh Nghiep
Nguyen, Thi Lan Phuong
Keywords: Product quantization
Hierarchical clustering tree
Approximate nearest search
Issue Date: 2019
Series/Report no.: Journal of Computer Science and Cybernetics;Vol.35 (01) .- P.69–83
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.
URI: http://dspace.ctu.edu.vn/jspui/handle/123456789/12147
ISSN: 1813-9663
Appears in Collections:Tin học và Điều khiển học (Journal of Computer Science and Cybernetics)

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