Creating descriptive visual words for tag ranking of compressed social image

Visual words description method has been widely applied in the fields of social image´s tag ranking, tag recommendation and annotation. At present, visual words are usually obtained by unsupervised clustering methods which lead to generate many unnecessary and non-descriptive words. Therefore, how to make visual words be descriptive has become a very meaningful task for tag ranking of social image. However, for compressed social image on the network, visual words are created after fully decompressing a compressed image into pixel domain. In this paper, creating descriptive visual words in compressed domain is proposed for tag ranking of compressed social image. Firstly, the traditional visual words are created by using the partly decoded data; then the descriptive visual words are selected from traditional visual words by the VisualWordRank ranking algorithm; finally the descriptive visual words are applied to rank the tag of social image. Experimental results show the descriptive visual words can improve the accuracy of tag ranking, which further prove our method has more descriptive ability. Besides that, our method also reduces the processing time for compressed social image greatly.