Texture-aware and structure-preserving superpixel segmentation

Abstract

Superpixel segmentation is an important pre-processing step in many computer vision applications. However, existing methods are mainly constructed on the basis of pixel-level features, which are generally vulnerable to the interference of strong gradient textures and fail to make the generated superpixels adhere to the object boundary well. Therefore, we bring forward a texture-aware and structure-preserving superpixel segmentation algorithm. First, we propose a cluster centroid oriented quarter-circular mask, which prevents the fine-scale structure pixels from sampling inconsistent texture pixels when gathering neighboring information from the local image patch. Next, we put forward a texture-suppressed and structure-preserving hybrid gradient. Its magnitude can well represent the probability that a pixel belongs to the structure one, and its direction can embody the main direction of image structures. Then, we design a color distance with texture-structure tradeoff strategy, which is based on the quarter-circular mask and the hybrid gradient direction. Finally, we devise an integrated structure-avoiding clustering distance, which can be obtained by seeking the maximum hybrid gradient magnitude along the linear path between a pixel and the superpixel centroid. It can effectively prevent the superpixel from crossing the object boundaries. Experimental results show that the superpixels generated by our algorithm not only adhere to object boundaries closely but also preserve regular shape. Our algorithm outperforms state-of-the-art methods, especially for the images with strong gradient textures.