Prior Knowledge Driven Energy for Saliency Detection

Saliency detection on images has experienced substantial progress in recent years on the basis of deep neural network (DNN). However, there may exist secondary saliency in the background that distracts DNN learning and mistakes the secondary salient regions as saliency. To address this issue, we propose a dual-term energy to improve the inference of saliency on top of DNN estimation, where dense term smoothens salient regions in pixel scale and sparse term extracts prior knowledge to differentiate saliency and non-saliency superpixels. Our prior knowledge including extra- and intra-region priors, contributes to improving overall saliency detection. The extra-region prior knowledge estimates the saliency probabilities for different pre-partitioned regions to eliminate the secondary saliency. The intra-region prior knowledge helps to group the salient regions that otherwise could be ignored by DNN predictor, and thus to provide more complete saliency definition. We evaluated our model on 8,465 images from four well-recognized saliency detection benchmarking datasets, and compared our model to six state-of-the-art comparative methods. Experimental results demonstrated that our model outperformed the state-of-the-art counterpart with improvements of up to 2.51% in terms of F-measure.