Please use this identifier to cite or link to this item:
Title: A study on parameter tuning for optimal indexing on large scale datasets
Authors: Dinh-Nghiep Le
Van-Thi Hoang
Duc-Toan Nguyên
The-Anh Pham
Keywords: Terms-Feature indexing
Approximate nearest neighbor search
Product quantization
Issue Date: 2020
Series/Report no.: Tạp chí Công nghệ Thông tin và Truyền thông;Số 03,CS.01 .- Tr.108-114
Abstract: Fast 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.
ISSN: 2525-2224
Appears in Collections:Khoa học Công nghệ Thông tin và Truyền thông

Files in This Item:
File Description SizeFormat 
  Restricted Access
2.18 MBAdobe PDF
Your IP:

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.