A novel image tag saliency ranking algorithm based on sparse representation

As the explosive growth of the web image data, image tag ranking used for image retrieval accurately from mass images is becoming an active research topic. However, the existing ranking approaches are not very ideal, which remains to be improved. This paper proposed a new image tag saliency ranking algorithm based on sparse representation. we firstly propagate labels from image-level to region-level via Multi-instance Learning driven by sparse representation, which means reconstructing the target instance from positive bag via the sparse linear combination of all the instances from training set, instances with nonzero reconstruction coefficients are considered to be similar to the target instance; then visual attention model is used for tag saliency analysis. Comparing with the existing approaches, the proposed method achieves a better effect and shows a better performance.