基于语义区域提取的图像重排

陈曾; 侯进; 张登胜; 张华忠

doi:10.3724/SP.J.1004.2011.01356

基于语义区域提取的图像重排

doi: 10.3724/SP.J.1004.2011.01356

陈曾¹,
侯进^1, ,,
张登胜²,
张华忠¹

1.
西南交通大学信息科学与技术学院成都 610031, 中国;
2.
莫纳什大学吉普斯兰信息技术学院丘吉尔维多利亚 3842, 澳大利亚

详细信息

通讯作者:
侯进西南交通大学信息科学与技术学院副教授.主要研究方向为多媒体检索与人机交互. E-mail: jhou@swjtu.edu.cn

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出版历程
- 收稿日期: 2010-04-25
- 修回日期: 2011-05-31
- 刊出日期: 2011-11-20

Image Re-ranking Based on Extraction of Semantic Regions

1.
School of Information Science and Technology, Southwest Jiaotong University, Chengdu 610031, P.R. China;
2.
Gippsland School of Info Tech, Monash University, Churchill, Victoria 3842, Australia

摘要

摘要: 针对目前图像搜索引擎难以正确把握用户真正意图的问题, 从爬虫Web图像搜索引擎检索结果入手,提出三种聚类算法来提取海量Web图像中的语义区域. 这三种聚类算法包括确定初始化中心的K-means聚类、确定参数的最大期望聚类以及基于半监督的K-means聚类算法. 然后选取显著值较大的显著区域作为语义区域.实验分析比较了三种聚类算法的有效性, 最终实现的图像重排系统能比网络搜索引擎更好地反馈给用户精确而且有序的查询结果.
- 语义区域提取 /
- 半监督聚类 /
- K-means聚类 /
- 最大期望聚类 /
- 图像重排
Abstract: It is difficult for current image search engines to accurately grasp the real intention of users. Based on the search results, we propose three clustering algorithms to extract semantic regions of Web images. These methods include K-means clustering with determined k centers, expectation maximization clustering with the determined parameters, and semi-supervised K-means clustering. We then select the salient regions with the high salient scores as the semantic regions. We demonstrate the experimental results by comparing the three clustering algorithms. The proposed image re-ranking system can more accurately show the ordered search results than web image engines.
- Extraction of semantic regions /
- semi-supervised clustering /
- K-means clustering /
- expectation maximization clustering /
- image re-ranking

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参考文献(1)

[1]

Cui J, Wen F, Tang X. Real time Google and live image search re-ranking. In: Proceedings of the 16th ACM International Conference on Multimedia. Vancouver, Canada: ACM, 2008. 729-732[2] Vijayanarasimhan S, Grauman K. Keywords to visual categories: multiple-instance learning for weakly supervised object categorization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Anchorage, USA: IEEE, 2008. 1-8[3] Berg T L, Forsyth D A. Animals on the web. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. New York, USA: IEEE, 2006. 1463-1470[4] Sun Y, Shimada S, Taniguchi Y, Kojima A. A novel region-based approach to visual concept modeling using web images. In: Proceedings of the 16th ACM International Conference on Multimedia. Vancouver, Canada: ACM, 2008. 635-638[5] Napoleon D, Lakshmi P G. An enhanced K-means algorithm to improve the efficiency using normal distribution data points. International Journal on Computer Science and Engineering, 2010, 2(7): 2409-2413[6] Govaert G, Nadif M. An EM algorithm for the block mixture model. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(4): 643-647[7] Gao Yan-Yu, Yin Yi-Xin, Uozumi T. A hierarchical image annotation method based on SVM and semi-supervised EM. Acta Automatica Sinica, 2010, 36(7): 960-967(高彦宇, 尹怡欣, Uozumi T. 一种基于支持向量机和半监督期望最大化算法的分级图像标识方法. 自动化学报, 2010, 36(7): 960-967)[8] Feng S L, Manmatha R, Lavrenko V. Multiple Bernoulli relevance models for image and video annotation. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington D. C., USA: IEEE, 2004. 1002-1009

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