Abstract
This paper presents a novel computational model for saliency detection. The proposed model utilizes feature level fusion method to integrate different kinds of visual features. The integrated features are used to measure saliency, so no separate feature conspicuity maps, or the subsequent combination of them is needed in our model. Then, the new model combines the local and global measurements for estimating saliency (termed LGMES) by using local and global kernel density estimations during the saliency computation process. Experimental results on two human eye fixation datasets demonstrate that the proposed model outperforms the state-of-the-art methods. Meanwhile, the proposed saliency measurement is more efficient than those methods using separately local or global measurements.