Please use this identifier to cite or link to this item: https://dspace.ctu.edu.vn/jspui/handle/123456789/12147
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dc.contributor.authorPham, The Anh-
dc.contributor.authorLe, Dinh Nghiep-
dc.contributor.authorNguyen, Thi Lan Phuong-
dc.date.accessioned2019-08-30T02:37:02Z-
dc.date.available2019-08-30T02:37:02Z-
dc.date.issued2019-
dc.identifier.issn1813-9663-
dc.identifier.urihttp://dspace.ctu.edu.vn/jspui/handle/123456789/12147-
dc.description.abstractThis 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.vi_VN
dc.language.isoenvi_VN
dc.relation.ispartofseriesJournal of Computer Science and Cybernetics;Vol.35 (01) .- P.69–83-
dc.subjectProduct quantizationvi_VN
dc.subjectHierarchical clustering treevi_VN
dc.subjectApproximate nearest searchvi_VN
dc.titleProduct sub-vector quantization for feature indexingvi_VN
dc.typeArticlevi_VN
Appears in Collections:Tin học và Điều khiển học (Journal of Computer Science and Cybernetics)

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