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Adaptive Tag Ranking based on Saliency Analysis
ZHAO Ripeng 1 #,SONG Zehai 2 *,FENG Songhe 2
1.School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044
2.School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044
*Correspondence author
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Funding: Nature Science Foundation of China （No.No.61100142）
Opened online: 4 May 2012
Accepted by: none
Citation: ZHAO Ripeng,SONG Zehai,FENG Songhe.Adaptive Tag Ranking based on Saliency Analysis[OL]. [ 4 May 2012] http://en.paper.edu.cn/en_releasepaper/content/4476147
 
 
Tag ranking has become a hot research topic due to its importance for image analysis and retrieval. Existing annotation methods about tag ranking can be roughly classified into two categories: tag relevance ranking and tag saliency ranking. Both methods have pros and cons. In this paper, we propose an adaptive tag ranking based on saliency analysis which combines the advantages of tag relevance ranking and tag saliency ranking. The main idea behind the approach is apparently simple. In short, to the given image, we first carry out image salient region detection and image saliency analysis by machine learning techno-logies like Support Vector Machine (SVM). If there exist visually salient regions of the given image, the corresponding annotated tags can be ranked according to the saliency property of the corresponding visual content; else tags can be ranked according to the relevance scores to the content of the image. The performance will be better than existing methods. To demonstrate the effectiveness and efficiency of the proposed algorithm, we do experiments on the COREL and MSRC image datasets.
Keywords:tag relevance ranking; tag saliency ranking; adaptive tag ranking
 
 
 

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