Saliency-based superpixels
Superpixels provide an over-segmentation representation of a natural image. However, they lack information of the entire object. In this paper, we propose a method to obtain superpixels through a merging strategy based on the bottom-up saliency values of superpixels. The proposed method aims to obtain meaningful superpixels, i.e., make the objects as complete as possible. The proposed method creates an over-segmented representation of an image. The saliency value of each superpixel is calculated through a biologically plausible saliency model in a way of statistical theory. Two adjacent superpixels are merged if the merged superpixel is more salient than the unmerged ones. The merging process is performed in an iterative way. Experimental evaluation on test images shows that the obtained saliency-based superpixels can extract the salient objects more effectively than the existing methods.
superpixel merging strategy saliency model visual attention