我們介紹一個關於向量量化與維度縮減的新觀察。此觀察能夠幫助改進近似最近鄰近搜索的品質。根據此觀察我們發展一個利用量化誤差與均衡子空間之方差準則的高效演算法。實驗結果顯示我們的方法在搜索大型資料組能夠達到更好的表現。我們也利用我們的快速近似最近鄰近搜索方法呈現一個應用。

We introduce a new observation about vector quantization and dimensionality reduction. This observation can help to improve the quality of approximate nearest neighbor search. Based on the observation we develop an efficient algorithm leveraging the quantization error and the balanced variances of subspaces criteria for codebook learning. Experimental results show that our approach is able to achieve better performance on searching large datasets. We also present an application that takes advantage of fast approximate nearest neighbor search with our approach.

1 Introduction 8
2 Background 11
2.1 Product Quantization . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.2 Optimized Product Quantization . . . . . . . . . . . . . . . . . . . . 12
3 Truncated Quantization 14
3.1 Dimension Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
3.2 Codebook Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
4 Experiments 18
4.1 Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
4.2 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
4.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
4.3.1 Retrieval performance . . . . . . . . . . . . . . . . . . . . . . 19
4.3.2 Criterion effectiveness . . . . . . . . . . . . . . . . . . . . . . 19
4.3.3 Encoding time . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
5 Application 24
5.1 Proposal Retrieval . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
5.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
6 Conclusion 29

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