Image Tag Refinement Using Tag Semantic and Visual Similarity
Social tagging on online websites provides users interfaces of describing resources with their own tags, and vast user-provided image tags facilitate image retrieval and management. However, these tags are often not related to the actual image content, affecting the performance of tag related applications. In this paper, a novel approach to automatically refine the image tags is proposed. Firstly, information entropy of the tag is defined to refine tag frequency to predict tag initial relevance. Then, tag correlation is calculated from two sides. One side is to measure semantic similarity of tag pairs using the structured information of the free encyclopedia Wikipedia. The other one is to compute the visual similarity of tag pairs based on the visual representation of the tag. Finally, to rerank the original tags, a fast random walk with restart is used and the top ones are reserved as the final tags. Experimental results conducted on dataset NUS-WIDE demonstrate the promising effectiveness of our approach.
semantic similarity social tagging tag refinement visual similarity
Wengang Cheng Xiaolei Wang
School of Control and Computer Engineering North China Electric Power University Beijing, China
国际会议
哈尔滨
英文
2146-2149
2011-12-24（万方平台首次上网日期，不代表论文的发表时间）