Texture-Aware Superpixel Segmentation
This addresses the issue of texture-aware segmentation for computer vision applications, but it is incremental as it builds on existing superpixel methods.
The paper tackles the problem of superpixel segmentation failing to group pixels with similar local texture properties by proposing a Texture-Aware SuperPixel (TASP) method that automatically adjusts spatial constraints based on local feature variance and uses a patch-based distance for texture homogeneity, resulting in outperforming state-of-the-art methods on texture and natural color image datasets.
Most superpixel algorithms compute a trade-off between spatial and color features at the pixel level. Hence, they may need fine parameter tuning to balance the two measures, and highly fail to group pixels with similar local texture properties. In this paper, we address these issues with a new Texture-Aware SuperPixel (TASP) method. To accurately segment textured and smooth areas, TASP automatically adjusts its spatial constraint according to the local feature variance. Then, to ensure texture homogeneity within superpixels, a new pixel to superpixel patch-based distance is proposed. TASP outperforms the segmentation accuracy of the state-of-the-art methods on texture and also natural color image datasets.