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.