Combining background information and a top-down model for computing salient objects

Predicting the salient object region in real scenes has progressed significantly in recent years. In this work, we propose a novel method for computing salient object regions by combining background information and a top-down visual saliency model, which is well-suited for locating category-specific salient objects in cluttered real scenes. First, we used a robust background measure to acquire clean saliency maps by optimizing background information. Second, we learned a top-down saliency object model by combining a class-specific codebook and conditional random fields (CRFs) during the training phase. Furthermore, our model used the locality-constrained linear codes as latent CRF variables. Finally, we computed salient object regions by combining the robust background measure and top-down model. Experimental results on the Graz-02 and PASCAL VOC2007 datasets show that our method creates much better saliency maps than current state-of-the-art methods.