UNSUPERVISED SEGMENTATION USING DYNAMIC SUPERPIXEL RANDOM WALKS

This paper presents a new segmentation method that combines random walks with superpixels. In this method, the coarseness of the segmentation is controlled using a single parameter. By using superpixels, the method can recompute the segmentation efficiently, making the parameter tuning process interactive. Moreover, an efficient strategy is proposed to adjust dynamically the parameters to the image's content, making our method more robust than existing approaches. Experiments performed on the Berkeley BSD300 segmentation database show our interactive method to outperform state-of-the-art approaches for this task.