Correlated multi-label refinement for semantic noise removal

Authors:
Tie Hua Zhou;Ling Wang;Ho Sun Shon;Yang Koo Lee;Keun Ho Ryu
Affiliations:
Database/Bioinformatics Laboratory, School of Electrical & Computer Engineering, Chungbuk National University, Chungbuk, Korea;Database/Bioinformatics Laboratory, School of Electrical & Computer Engineering, Chungbuk National University, Chungbuk, Korea;Database/Bioinformatics Laboratory, School of Electrical & Computer Engineering, Chungbuk National University, Chungbuk, Korea;Database/Bioinformatics Laboratory, School of Electrical & Computer Engineering, Chungbuk National University, Chungbuk, Korea;Database/Bioinformatics Laboratory, School of Electrical & Computer Engineering, Chungbuk National University, Chungbuk, Korea
Venue:
ICIC'10 Proceedings of the Advanced intelligent computing theories and applications, and 6th international conference on Intelligent computing
Year:
2010

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Abstract

Images are major source of Web content. Image annotation is an important issue which is adopted to retrieve images from large image collections based on the keyword annotations of images, which access a large image data-base with textual queries. With surrounding text of Web images increasing, there are generally noisy. So, an efficient image annotation approach for image retrieval is highly desired, which requires effective image search techniques. The developing clustering technologies allow the browsing and retrieval of images with low cost. Image search engines retrieved thousands of images for a given query. However, these results including a significant number of semantic noisy. In this paper, we proposed a new clustering algorithm Double-Circles that enable to remove noisy results and explicitly exploit more precise representative annotations. We demonstrate our approach on images collected from Flickr engine. Experiments conducted on real Web images present the effectiveness and efficiency of the proposed model.