Salient Object Detection Using Spatially Weighted Multiple Contrast Cues

Detecting objects that capture visual attention has played a key role in computer vision. In this paper, we present a model that incorporates multiple bottom-up cues depending on the following concepts: connectivity to the outstanding parts where each superpixel will take a saliency score based on its connectivity strength to the enclosed interest regions, global contrast by measuring how every superpixel differs from all superpixels in the image and utilizing regional frequency tuning and center-bias. The final saliency map is produced by integrating the resulted foreground maps of each cue and refining the result with the optimization framework. An extensive experimental evaluation is done on three challenging datasets to evaluate the proposed model using common classification criteria. Our model has the superiority in performance over the other models qualitatively and quantitatively.