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2016 Fiscal Year Final Research Report

Large Scale Nearest Neighbor Search by Acceleration an Stabilization of Product Quantization

Research Project

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Project/Area Number 15K12025
Research Category

Grant-in-Aid for Challenging Exploratory Research

Allocation TypeMulti-year Fund
Research Field Multimedia database
Research InstitutionThe University of Tokyo

Principal Investigator

Aizawa Kiyoharu  東京大学, 大学院情報理工学系研究科, 教授 (20192453)

Project Period (FY) 2015-04-01 – 2017-03-31
Keywords直積量子化 / 最近傍探索
Outline of Final Research Achievements

Product Quantization (PQ) is the most promising technique for high-speed nearest neighbor search for large-scale images retrieval. For this product quantization, we carried out the following research in order to dramatically increase computational efficiency and memory efficiency.
(1)Dense space partitioning PQ: Efficient dense space partitioning by combining multiple centroids of clusters (2)PQTable: Efficient product quantization using PQ code as a hash table (3)PQkmeans: Fast and memory efficient kmeans in PQ domain achieving very large scale clustering (4)Residual Expansion Algorithm: Efficient and Effective Optimization for non-convex least square problem such as kmeans

Free Research Field

マルチメディア、画像処理、コンピュータビジョン

URL: 

Published: 2018-03-22  

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