Improving social tag-based image retrieval with CBIR technique

With the popularity of social image-sharing websites, the amount of images uploaded and shared among the users has increased explosively. To allow keyword search, the system constructs an index from image tags assigned by the users. The tag-based image retrieval approach, although very scalable, has some serious drawbacks due to the problems of tag spamming and subjectivity in tagging. In this paper, we propose an approach for improving the tag-based image retrieval by exploiting some techniques in content-based image retrieval (CBIR). Given an image collection, we construct an index based on 130-scale Munsell-based colors. Users are allowed to perform query by keywords with color and/or tone selection. The color index is also used for improving ranking of search results via the user relevance feedback.