Salient Region Detection via Discriminative Dictionary Learning and Joint Bayesian Inference

In past decades, saliency detection has received increasing attention from computer vision communities, for its potential usage in many vision-related tasks. However, finding representative and discriminative features to accurately locate salient regions from complex scenes remains a challenging problem. Recent research on primary visual cortex (V1) shows that vision neurons are sparsely connected to form a compact representation of natural scenes and different visual stimuli are processed separately according to their semantic importance. Inspired by the above characteristics of visual perception, in this paper we advance a novel saliency detection method via representative and discriminative dictionary learning. An assumption that salient and nonsalient information are sparsely coded under two separate dictionaries is cast on the problem and we propose to learn a compact background dictionary from the image itself for saliency estimation. Different from previous methods, our saliency cues are obtained via active learning strategies rather than artificially designed rules, and thus is more adaptive. Followed by this, a probabilistic inference model is deduced to fully excavate multisource information about the scenes for high-quality saliency map generation. This joint inference scheme takes both spatial and color space information into consideration and is proved to be quite effective in practice. Finally, to investigate the performance of the proposed model, some experiments are conducted on two benchmark data sets along with other 20 state-of-the-art saliency detection approaches. The experimental results show that our method outperforms its counterparts and can correctly detect salient regions, even when other methods fail. Besides, the usability of the proposed method in real application-based cases is verified by applying it to content-based image resizing and promising results are obtained.