Optimal contrast based saliency detection

• An entropy based optimal contrast scheme for saliency detection.
• Performing the multi-scale enhancement to refine the results.
• Evaluations on two eye-tracking datasets and one human-annotated dataset.

Saliency detection has been gaining increasing attention in recent years since it could significantly boost many content-based multimedia applications. Most traditional approaches adopt the predefined local contrast, global contrast, or heuristic combination of them to measure saliency. In this paper, based on the underlying premises that human visual attention mechanisms work adaptively for various scales and salient objects can maximally pop out with respect to the background within a specific surrounding area, we propose a novel saliency detection method using a new concept of optimal contrast. A number of contrast hypotheses are first calculated with various surrounding areas by means of sparse coding principles. Afterwards, these hypotheses are compared using an entropy-based criterion and the optimal contrast is selected which is treated as the core factor for building the saliency map. Finally, a multi-scale enhancement is performed to further refine the results. Comprehensive evaluations on three publicly available benchmark datasets and comparisons with many up-to-date algorithms demonstrate the effectiveness of the proposed work.