Semantic superpixel extraction via a discriminative sparse representation

The superpixel extraction algorithm is becoming increasingly significant for pattern recognition applications. Different superpixel generation methods have different properties and lead to various over-segmentation results. In this paper, we treat the over-segmentation as an image decomposition problem, and propose a novel discriminative sparse coding (DSC) algorithm to effectively extract the semantic superpixels. Specifically, the DSC algorithm incorporates a new discriminative regularization term in the traditional sparse representation model. Then the new regularization term is combined with the reconstruction error and sparse constraint to form a unified objective function. The extracted superpixels not only respect the local image boundaries, but also are dissimilar between each other. Meanwhile, the quantity of segments is sparse. These properties benefit for the semantic superpixel extraction. The final refined superpixels are generated based on an effective Bayesian-classification criterion in a post-processing step. Experimental results show that the over-segmentation quality of DSC algorithm outperforms the state of the art methods.