[1]练浩,曾宪华,李淑芳.有监督全局流形排序的图像检索算法[J].智能系统学报,2014,9(01):92-97.[doi:10.3969/j.issn.1673-4785.201303021]
 LIAN Hao,ZENG Xianhua,LI Shufang.Supervised global manifold ranking based image retrieval algorithm[J].CAAI Transactions on Intelligent Systems,2014,9(01):92-97.[doi:10.3969/j.issn.1673-4785.201303021]
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第9卷
期数:
2014年01期
页码:
92-97
栏目:
出版日期:
2014-02-25

文章信息/Info

Title:
Supervised global manifold ranking based image retrieval algorithm
作者:
练浩 曾宪华 李淑芳
重庆邮电大学 计算智能重庆市重点实验室, 重庆 400065
Author(s):
LIAN Hao ZENG Xianhua LI Shufang
Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
关键词:
流形学习图像检索算法流形排序有监督流形排序不相关排序
Keywords:
manifold learningimage retrieval algorithmmanifold rankingsupervised manifold rankinguncorrelated ranking
分类号:
TP391
DOI:
10.3969/j.issn.1673-4785.201303021
摘要:
针对流形排序图像检索中无正反馈标记的查询查准率为零时, 查询无法得到改善的情况, 提出了有监督全局流形排序图像检索算法。该算法充分利用图像库中图像的局部与非局部的几何分布信息、标记信息以及用户的查询反馈信息来提高检索性能, 并且通过不相关排序对检索到的相关排序向量进行校正, 改善了第一次查询查准率为零的情况。在MSRA-MM图像库上的实验表明, SG-MRBIR算法明显改善了图像检索性能, 特别是第一次用户反馈后就使得平均查准率与广义流形排序图像检索算法(G-MRBIR)相比有所提高, 说明了该算法的有效性和优越性。
Abstract:
In manifold ranking based image retrieval, if there is no positive feedback, the situation where the precision ratio is zero can not be changed. So a new supervised global manifold ranking based image retrieval (SG-MRBIR) algorithm is proposed. For improving the image retrieval performance, this algorithm fully applies the local and non-local geometrical distribution of image datasets, image label information and user’s related feedback information. And the related ranking vectors of the retrieved image are corrected by using uncorrelated ranking. Finally, the situation where the first query precision is zero is improved. The experimental results for the MSRA-MM image database show that our SG-MRBIR algorithm distinctly improves the image retrieval performance. Especially, after the first feedback by users in the experiments, the average precision ratio of retrieving was increased as compared with the generalized manifold ranking based image retrieval (G-MRBIR) algorithm. This verifies the effectiveness and superiority of this algorithm.

参考文献/References:

[1] KATO T. Database architecture for content-based image retrieval[C]//Proceedings of the SPIE Vol. 1662-Image Storage and Retrieval Systems. San Jose, USA, 1992: 112-123.
[2] RITENDRA D, DHIRAJ J, LI Jia, et a1. Image retrieval: ideas influence, and trends of the new age[J]. ACM Computer Surveys, 2008, 40(2): Article No. 5.
[3] HE Xiaofei, NIYOGI P. Locality preserving projections[C]//Advances in Neural Information Processing Systems. Vancouver, Canada, 2003: 3-15.
[4] HE Xiaofei. Incremental semi-supervised subspace learning for image retrieval[C]//Proceedings of the 12th Annual ACM International Conference on Multimedia. New York, USA, 2004: 2-8.
[5] LIN Yenyu, LIU Tyngluh, CHEN Hwanntzong. Semantic manifold learning for image retrieval[C]//Proceedings of the 13th Annual ACM International Conference on Multimedia. Singapore, 2005: 249-258.
[6] HE Xiaofei, CAI Deng, HAN Jiawei. Learning a maximum margin subspace for image retrieval[J]. IEEE Transactions on Knowledge and Data Engineering, 2008, 20(2): 189-201.
[7] 刘利,韦佳,马千里.基于流形学习的图像检索研究进展[J].北京交通大学学报, 2010, 34(5): 164-171.LIU Li, WEI Jia, MA Qianli. State-of-the-art on image retrieval based on manifold learning[J]. Journal of Beijing Jiaotong University, 2010, 34(5): 164-171.
[8] ZHOU Dengyong, JASON W, ARTHUR G. et al. Ranking on data manifolds[C]//Advances in Neural Information Processing Systems. Vancouver, Canada, 2003: 169-176.
[9] HE Jingrui, LI Mingjing, ZHANG Hongjiang, et a1. Manifold-ranking based image retrieval[C]//Proceedings of the 12th Annual ACM International Conference on Multimedia. New York, USA, 2004: 9-16.
[10] HE Jingrui, LI Mingjing, ZHANG Hongjiang, et a1. Generalized manifold ranking based image retrieval[J]. IEEE Transactions on Image Process, 2006, 15(10): 3170-3177.
[11] XU Bin, BU Jiajun, CHEN Chen, et al. Efficient manifold ranking for image retrieval[C]//The 34th Annual ACM SIGIR Conference. Beijing, China, 2011: 24-28.

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备注/Memo

备注/Memo:
收稿日期:2013-03-14。
基金项目:国家自然科学基金资助项目(61075019,61203308);重庆市自然科学基金资助项目(CSTC-2010BB2406).
作者简介:练浩,男,1985年生,硕士研究生,主要研究方向为智能信息处理;李淑芳,女,1987年生,硕士研究生,主要研究方向为流形学习。
通讯作者:曾宪华,男,1973年生,副教授,博士,计算机学会会员,主要研究方向为流形学习、计算机视觉等.E-mail:xianhuazeng@gmail.com.
更新日期/Last Update: 1900-01-01