Color Image Segmentation Using Improved Graph Cuts
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摘要: 针对基于多标签图割的分割算法因标签过多造成计算量大的问题，提出一种基于多组件图割的彩色图像分割算法。首先根据彩色梯度信息融合所提取的四元数cut-off窗口特征和ClE Lab颜色特征进行特征提取;然后使用多组件图割算法结合最大似然(ML)估计自动分割图像，在迭代过程中，每一个分割内不相邻的区域将作为该分割的多个组件以减少标签数;最后去除一些弱边界得到分割结果。理论和实验结果表明，新算法不仅具有收敛性，而且分割性能优于原始算法。Abstract: A novel unsupervised color image segmentation method using graph cuts with multiple components is proposed，which can overcome the problem of the higher computational complexity caused by more labels during inferring by graph cuts. First，the quaternion cut-off window feature and CIE Lab color feature of a given image are extracted and fused based on the gradient information of the image. Then the segmentation is formulated as a labeling problem and solved by an iterative process based on graph cuts and maximum likelihood(ML) estimation. At each iteration，the connected regions in a segment are handled as sub-components of the segment instead of relabeling them with unique labels. In doing so，the number of labels does not increase，and thus the computational complexity can be reduced during inference by graph cuts. Finally，the segmentation result is obtained after removing some weak edges. Experimental results and theoretical proof demonstrate the good performance of the proposed method.
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