ITE Transactions on Media Technology and Applications

直積量子化を用いた近似最近傍探索に関するサーベイ

松井勇佑
国立情報学研究所
内田祐介
DeNA
Hervé Jégou
Facebook AI Research
佐藤真一
国立情報学研究所

  • André+, “Cache Locality is not Enough: High-performance Nearest Neighbor Search with Product Quantization Fast Scan”, VLDB 15
  • André+, “Accelerated Nearest Neighbor Search with Quick ADC”, ICMR 17
  • André+, “Quicker ADC : Unlocking the Hidden Potential of Product Quantization with SIMD”, IEEE TPAMI 20
  • Babenko and Lempitsky, “The Inverted Multi-index”, CVPR 12
  • Babenko and Lempitsky, “Additive Quantization for Extreme Vector Compression”, CVPR 14
  • Babenko and Lempitsky, “The Inverted Multi-index”, IEEE TPAMI 15
  • Babenko and Lempitsky, “Tree Quantization for Large-scale Similarity Search and Classification”, CVPR 15
  • Babenko and Lempitsky, “Efficient Indexing of Billion-scale Datasets of Deep Descriptors”, CVPR 16
  • Babenko and Lempitsky, “Product Split Trees”, CVPR 17
  • Bagherinezhad+, “LCNN: Lookup-based Convolutional Neural Network”, CVPR 17
  • Baranchuk+, “Revisiting the Inverted Indices for Billion-Scale Approximate Nearest Neighbors”, ECCV 18
  • Blalock and Guttag, “Bolt: Accelerated Data Mining with Fast Vector Compression”, KDD 17
  • Eghbali and Tahvildari, “Deep Spherical Quantization for Image Search”, CVPR 19
  • Douze+, “Polysemous Codes”, ECCV 16
  • Douze+, “Link and code: Fast Indexing with Graphs and Compact Regression Codes”, CVPR 18
  • Ge+, “Optimized Product Quantization”, IEEE TPAMI 14
  • Ge+, “Product Sparse Coding”, CVPR 14
  • He+, “K-means Hashing: An Affinity-preserving Quantization Method for Learning Binary Compact Codes”, CVPR 13
  • Heo+, “Short-list Selection with Residual-aware Distance Estimator for K-nearest Neighbor Search”, CVPR 16
  • Heo+, “Distance Encoded Product Quantization”, CVPR 14
  • Iwamura+, “What is the Most Efficient Way to Select Nearest Neighbor Candidates for Fast Approximate Nearest Neighbor Search?”, ICCV 13
  • Jain+, “Approximate Search with Quantized Sparse Representations”, ECCV 16
  • Jégou+, “Product Quantization for Nearest Neighbor Search”, IEEE TPAMI 11
  • Jégou+, “Aggregating Local Descriptors into a Compact Image Representation”, CVPR 10
  • Jégou+, “Searching in One Billion Vectors: Re-rank with Source Coding”, ICASSP 11
  • Johnson+, “Billion-scale Similarity Search with GPUs”, IEEE TBD 20
  • Klein and Wolf, “End-to-end Supervised Product Quantization for Image Search and Retrieval”, CVPR 19
  • Kalantidis and Avrithis, “Locally Optimized Product Quantization for Approximate Nearest Neighbor Search”, CVPR 14
  • Li+, “Online Variable Coding Length Product Quantization for Fast Nearest Neighbor Search in Mobile Retrieval”, IEEE TMM 17
  • Martinez+, “Revisiting Additive Quantization”, ECCV 16
  • Martinez+, “LSQ++: Lower Running Time and Higher Recall in Multi-codebook Quantization”, ECCV 18
  • Matsui+, “PQTable: Fast Exact Asymmetric Distance Neighbor Search for Product Quantization using Hash Tables”, ICCV 15
  • Matsui+, “PQk-means: Billion-scale Clustering for Product-quantized Codes”, ACMMM 17
  • Matsui+, “Reconfigurable Inverted Index”, ACMMM 18
  • Ning+, “Scalable Image Retrieval by Sparse Product Quantization”, IEEE TMM 17
  • Norouzi and Fleet, “Cartesian k-means”, CVPR 13
  • Ozan+, “Competitive Quantization for Approximate Nearest Neighbor Search”, IEEE TKDE 16
  • Spyromitros-Xious+, “A Comprehensive Study over VLAD and Product Quantization in Large-scale Image Retrieval”, IEEE TMM 14
  • Yu+, “Product Quantization Network for Fast Image Retrieval”, ECCV 18
  • Yu+, “Generative Adversarial Product Quantization”, ACMMM 18
  • Wang+, “Optimized Distances for Binary Code Ranking”, ACMMM 14
  • Wang+, “Optimized Cartesian k-means”, IEEE TKDE 15
  • Wang+, “Supervised Quantization for Similarity Search”, CVPR 16
  • Wang and Zhang, “Composite Quantization”, IEEE TPAMI 19
  • Wieschollek+, “Efficient Large-scale Approximate Nearest Neighbor Search on the GPU”, CVPR 16
  • Wu+, “Multiscale Quantization for Fast Similarity Search”, NIPS 17
  • Wu+, “Quantized Convolutional Neural Networks for Mobile Devices”, CVPR 16
  • Xia+, “Joint Inverted Indexing”, ICCV 13
  • Zhang+, “Composite Quantization for Approximate Nearest Neighbor Search”, ICML 14
  • Zhang+, “Sparse Composite Quantization”, CVPR 15.
  • Zhang+, “Collaborative Quantization for Crossmodal Similarity Search”, CVPR 16
  • Zhang+, “Efficient Large-scale Approximate Nearest Neighbor Search on OpenCL FPGA”, CVPR 18

Abstract

直積量子化 (Product Quantization; PQ) およびその発展系は，近似最近傍探索の文脈において人気があり成功を収めている 手法です．本稿では，その基本的なアルゴリズムを紹介し，実行可能なサンプルコードを提供します． そして，PQに関する包括的なサーベイを行います．

Publication

BibTeX

@article{ite_matsui_2018,
    title={A Survey of Product Quantization},
    author={Yusuke Matsui and Yusuke Uchida and Herv\'{e} J\'{e}gou and Shin'ichi Satoh},
    journal={ITE Transactions on Media Technology and Applications},
    volume={6},
    number={1},
    pages={2--10},
    year={2018},
}