An improved segmentation algorithm of color image in complex background based on graph cuts
Hanyu Hong, Xiang‐Yun Guo, Xiuhua Zhang · 2011
Recently, it is still difficult to extract interested object from complex background. In this field, interactive image segmentation method has attracted much attention in the vision. In this paper, we propose a new algorithm to segment the interested object from complex background. In the algorithm, we use the improved K-means clustering in the LUV color space to get more accurate classifications of the labeled pixels. Then, build up energy function model and calculate the energy of segmentation properly. Finally, we get the perfect result through graph cuts and denoising algorithm based on connected components.