Large Scale Nearest Neighbor Search by Acceleration an Stabilization of Product Quantization
| Project/Area Number |
15K12025
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| Research Category |
Grant-in-Aid for Challenging Exploratory Research
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| Allocation Type | Multi-year Fund |
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| Research Field |
Multimedia database
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| Research Institution | The University of Tokyo |
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Principal Investigator |
Aizawa Kiyoharu 東京大学, 大学院情報理工学系研究科, 教授 (20192453)
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| Project Period (FY) |
2015-04-01 – 2017-03-31
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| Project Status |
Completed (Fiscal Year 2016)
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| Budget Amount *help |
¥3,250,000 (Direct Cost: ¥2,500,000、Indirect Cost: ¥750,000)
Fiscal Year 2016: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
Fiscal Year 2015: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
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| Keywords | 直積量子化 / 最近傍探索 / 大規模検索 / 検索効率 / メモリ効率 / 非凸最適化 / Kmeans / 大規模画像検索 |
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| 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
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Report
(3 results)
Research Products
(6 results)
