Transferring Segmentation from Image to Image via Contextual Sparse Representation.

It is still a fundamental task to segment objects out from diverse background. To tackle this task, we propose a transferring segmentation framework, which aims to automatically segment new images when a single segmented example is given. Our segmentation approach is developed under the observation that some regions of foreground and background are often very similar but rarely share similar contextual information. To this end, we propose to construct a contextual dictionary by incorporating neighboring information as context. The segmentation task is finally accomplished in way of supervised classification via sparse representation with the constructed contextual dictionary. Experimental results on diverse natural images demonstrate that the proposed method achieves favorable results in both visual quality and accuracy.