Towards relevance and saliency ranking of image tags

Social image tag ranking has emerged as an important research topic recently due to its potential application on web image search. This paper presents an adaptive all-season tag ranking algorithm which can handle the images with and without distinct object(s) using different tag ranking strategies. Firstly, based on saliency map derived from the visual attention model, a linear SVM is trained to pre-classify an image as attentive or non-attentive category by using the gray histogram descriptor on the corresponding saliency map. Then, an image with distinct object is processed by an attention-driven tag saliency ranking algorithm emphasizing distinct object. On the other hand, an image without distinct object is processed by the tag relevance ranking algorithm via the sparse representation based neighbor-voting strategy. Such adaptive ranking strategy can be regarded as taking full advantage of existing tag ranking paradigms. Experiments conducted on well-known image data sets demonstrate the effectiveness and efficiency of the proposed framework.