Generalized residual vector quantization for large scale data
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| Veröffentlicht in: | arXiv.org (Sep 17, 2016), p. n/a |
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Cornell University Library, arXiv.org
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| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 2080356711 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2331-8422 | ||
| 035 | |a 2080356711 | ||
| 045 | 0 | |b d20160917 | |
| 100 | 1 | |a Liu, Shicong | |
| 245 | 1 | |a Generalized residual vector quantization for large scale data | |
| 260 | |b Cornell University Library, arXiv.org |c Sep 17, 2016 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a Vector quantization is an essential tool for tasks involving large scale data, for example, large scale similarity search, which is crucial for content-based information retrieval and analysis. In this paper, we propose a novel vector quantization framework that iteratively minimizes quantization error. First, we provide a detailed review on a relevant vector quantization method named \textit{residual vector quantization} (RVQ). Next, we propose \textit{generalized residual vector quantization} (GRVQ) to further improve over RVQ. Many vector quantization methods can be viewed as the special cases of our proposed framework. We evaluate GRVQ on several large scale benchmark datasets for large scale search, classification and object retrieval. We compared GRVQ with existing methods in detail. Extensive experiments demonstrate our GRVQ framework substantially outperforms existing methods in term of quantization accuracy and computation efficiency. | |
| 653 | |a Euclidean space | ||
| 653 | |a Feedback control systems | ||
| 653 | |a Information retrieval | ||
| 653 | |a Computing time | ||
| 653 | |a Vector quantization | ||
| 700 | 1 | |a Shao, Junru | |
| 700 | 1 | |a Lu, Hongtao | |
| 773 | 0 | |t arXiv.org |g (Sep 17, 2016), p. n/a | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/2080356711/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u http://arxiv.org/abs/1609.05345 |