Generalized residual vector quantization for large scale data

Збережено в:

Бібліографічні деталі
Опубліковано в::	arXiv.org (Sep 17, 2016), p. n/a
Автор:	Liu, Shicong
Інші автори:	Shao, Junru, Lu, Hongtao
Опубліковано:	Cornell University Library, arXiv.org
Предмети:	Euclidean space Feedback control systems Information retrieval Computing time Vector quantization
Онлайн доступ:	Citation/Abstract Full text outside of ProQuest
Теги:	Додати тег Немає тегів, Будьте першим, хто поставить тег для цього запису!

Опис
Короткий огляд:	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.
ISSN:	2331-8422
Джерело:	Engineering Database