基于潜在主题的分布式视觉检索模型

doi:10.3969/j.issn.1000-3428.2012.24.035

计算机工程 ›› 2012, Vol. 38 ›› Issue (24): 146-151. doi: 10.3969/j.issn.1000-3428.2012.24.035

基于潜在主题的分布式视觉检索模型

陈宜明 ^1,2,3，段凌宇 ¹，黄艳 ^2,3，李冰 ^1,3，林杰 ¹，黄铁军¹

(1. 北京大学信息科学技术学院，北京 100871；2. 中国科学院沈阳计算技术研究所，沈阳 110168；3. 中国科学院研究生院，北京 100049)

收稿日期:2012-03-12 修回日期:2012-04-22 出版日期:2012-12-20 发布日期:2012-12-18
作者简介:陈宜明(1985－)，男，硕士研究生，主研方向：分布式检索，视觉检索；段凌宇，副教授；黄艳，副研究员；李冰、林杰，博士研究生；黄铁军，教授
基金资助:
国家自然科学基金资助项目(60902057)；国家“973”计划基金资助项目(2009CB320902)

Distributed Visual Retrieval Model Based on Potential Theme

CHEN Yi-ming ^1,2,3, DUAN Ling-yu ¹, HUANG Yan ^2,3, LI Bing ^1,3, LIN Jie¹, HUANG Tie-jun ¹

(1. School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China; 2. Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang 110168, China; 3. Graduate University of Chinese Academy of Sciences, Beijing 100049, China)

Received:2012-03-12 Revised:2012-04-22 Online:2012-12-20 Published:2012-12-18

摘要/Abstract

摘要： 为将文档聚类划分的分布式检索方法直接应用于视觉检索领域，提出一种基于潜在主题的分布式视觉检索模型。给出模型框架，包括图像视觉单词的数据集划分方法和图像子集选择方法，以此优化图像分布式检索性能。实验结果表明，该模型在不降低检索准确率的前提下，能优先选择少量的图像子集进行检索，并提高查询的吞吐量。

关键词: 分布式检索, 视觉检索, 词袋模型, 图像数据集划分, 图像数据集选择, 潜在主题

Abstract: In order to use distributed retrieval method for document clustering partition directly in visual search field, this paper proposes a distributed visual retrieval model based on potential theme. This paper gives the model framework, including dataset partition method of image visual words and image subset selection method, to optimize image distributed retrieval performance. Experimental results show that this model searches in a few selected image collections without losing retrieval accuracy and improves the query throughput.

Key words: distributed retrieval, visual retrieval, Bag of Word(BoW) model, image dataset division, image dataset selection, potential theme

中图分类号:

TP311

陈宜明, 段凌宇, 黄艳, 李冰, 林杰, 黄铁军. 基于潜在主题的分布式视觉检索模型[J]. 计算机工程, 2012, 38(24): 146-151.

CHEN Yi-Meng, DUAN Ling-Yu, HUANG Yan, LI Bing, LIN Jie, HUANG Tie-Jun. Distributed Visual Retrieval Model Based on Potential Theme[J]. Computer Engineering, 2012, 38(24): 146-151.

http://www.ecice06.com/CN/Y2012/V38/I24/146

参考文献

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[3] Milad S. Central-rank-based Collection Selection in Uncooperative Distributed Information Retrieval[C]//Proc. of the 29th European Conference on IR Research. Berlin, Germany: Springer-Verlag, 2007.
[4] Luo Si, Rong Jin, Jamie C, et al. A Language Modeling Framework for Resource Selection and Results Merging[C]//Proc.
of the 11th International Conference on Information and Knowledge Management. New York, USA: ACM Press, 2002.
[5] Xu Jinxi, Croft W B. Cluster-based Language Models for Distributed Retrieval[C]//Proc. of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, USA: ACM Press, 1999.
[6] Puppin D, Silvestri F, Laforenza D. Query-driven Document Partitioning and Collection Selection[C]//Proc. of the 1st International Conference on Scalable Information Systems. New York, USA: ACM Press, 2006.
[7] Ricardo B Y, Castillo C, Flavio J, et al. Challenges in Distributed Information Retrieval[EB/OL]. (2010-11-21). http://www.citeulike. org/user/ChaTo/article/966384.
[8] 于林森, 张田文, 张开越. 图像检索中的相似性判别及索引方法综述[J]. 小型微型计算机系统, 2007, 28(2): 356-360.
[9] Bosch A, Zisserman A, Munoz X. Scene Classification Using a Hybrid Generative/Discriminative Approach[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008, 30(4): 712-727.
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