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dc.contributor.authorDinh-Nghiep Le-
dc.contributor.authorVan-Thi Hoang-
dc.contributor.authorDuc-Toan Nguyên-
dc.contributor.authorThe-Anh Pham-
dc.description.abstractFast matching is a crucial task in many Computer Vision applications due to its computationally intensive overhead. especially for high feature spaces. Promising techniques to address this problem have been investigated in the literature such as product quantization, hierarchical clustering decomposition, etc. In these approaches, a distance metric must be learned to support the ro-ranking step that helps filter out the best candidates. Nonetheless, computing the distances is a much intensively computational task and is often done during the Online search phase. As a result, this process degrades the search performance. In this work, we conduct a study on parameter tuning to rnakc efficicnt the computation of distances. Different searching strategies are also investigated to justify the impact of coding quality on search performance. Experiments have been conducted in a Standard product quantization framework and showed interesting results in terms of both coding quality and search efficiency.vi_VN
dc.relation.ispartofseriesTạp chí Công nghệ Thông tin và Truyền thông;Số 03,CS.01 .- Tr.108-114-
dc.subjectTerms-Feature indexingvi_VN
dc.subjectApproximate nearest neighbor searchvi_VN
dc.subjectProduct quantizationvi_VN
dc.titleA study on parameter tuning for optimal indexing on large scale datasetsvi_VN
Appears in Collections:Khoa học Công nghệ Thông tin và Truyền thông

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