Robust Image Segmentation using Contour-guided Color Palettes

The contour-guided color palette (CCP) 1 is proposed for robust image segmentation. It efficiently integrates con- tour and color cues of an image. To find representative colors of an image, color samples along long contours be- tween regions, similar in spirit to machine learning method- ology that focus on samples near decision boundaries, are collected followed by the mean-shift (MS) algorithm in the sampled color space to achieve an image-dependent color palette. This color palette provides a preliminary segmen- tation in the spatial domain, which is further fine-tuned by post-processing techniques such as leakage avoidance, fake boundary removal, and small region mergence. Segmenta- tion performances of CCP and MS are compared and an- alyzed. While CCP offers an acceptable standalone seg- mentation result, it can be further integrated into the frame- work of layered spectral segmentation to produce a more robust segmentation. The superior performance of CCP- based segmentation algorithm is demonstrated by experi- ments on the Berkeley Segmentation Dataset.