Texture-Aware Superpixel Segmentation
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Most superpixel methods are based on spatial and color measures at the pixel level. Therefore, they can highly fail to group pixels with similar local texture properties, and need fine parameter tuning to balance the two measures. In this paper, we address these issues with a new Texture-Aware SuperPixel (TASP) segmentation method. TASP locally adjusts its spatial regularity constraint according to the feature variance to accurately segment both smooth and textured areas. A new pixel to superpixel patch-based distance is also proposed to ensure texture homogeneity within created regions. TASP substantially outperforms the segmentation accuracy of state-of-the-art methods on both natural color and texture images.
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