Color images segmentation using new definition of connected components

This paper proposes a definition of the connected components of color images, (ε_H, ε_C)-connected components ((ε_H, ε_C)-CCs). A systematic segmentation method using (ε_H, ε_C )-CCs is also presented. The similarity of pixels is measured in the IHC (intensity, hue and chroma) color space, which was proposed by the authors previously, and the similar pixels in a given image are grouped into (ε_H, ε_C)-CCs. Experiment results demonstrate that color images can be effectively segmented in accordance with the perception of human using the definition of (ε _H, ε_C)-CCs and the systematic method. A hybrid system composed of MEBML (mind-evolution-based machine learning) and MLCNN (maximum likelihood clustering neural network) is used to cluster features of small windows of an image. The hybrid system has good performances on clustering and makes the color images segmentation algorithm efficient