  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.
