A CRF based approach to combine features for saliency map extraction

Detecting objects most focused on by human eyes when viewing a scene is of great interest to the computer vision community. Although a large amount of detection algorithms are available, due to variety and complexity of the image structures, the obtained saliency maps are still not satisfying enough. In this paper, we have proposed an efficient, supervised algorithm for saliency map detection which uses a conditional random field. Integrating different salient cues with matrix decomposition methods through CRF is one of the innovations of this paper. Another achievement of this paper is considering potential weights, obtained from CRF training process, as a ranking tool to select the best saliency cues. Since CRF is a supervised method, some papers select, for training step, a number of images which are most similar to the input image. The present paper offers, as our third contribution, a comprehensive assessment of the methods which select such similar images. Evaluating the proposed method on the ECSSD and MSRA-10k datasets with respect to the evaluation criteria has indicated its excellent performance.