基于色度直方图的颜色聚类算法

提取目标背景的主色是迷彩设计中的重要步骤，通常采用的颜色聚类算法具有监督性的缺陷. 为此，提出一种基于色度直方图的、无监督的颜色聚类算法. 该算法采用CIE 1931色度系统建立色度直方图，根据像素点在该坐标系的分布规律自动生成聚类中心. 逐一计算像素点与各聚类中心的色度的欧氏距离，将像素点与最近的聚类中心归于一类. 实验结果表明，采用该聚类算法能够准确提取主色，自动分割彩色图像，且比普通聚类算法的时间效率更优.

Color Clustering Based on Chromaticity Histogram

In camouflage design, extracting the dominant color from target background is an important step. A drawback of common color clustering methods is the requirement of supervision. This paper proposes an unsupervised color clustering algorithm based on the chromaticity histogram. The chromaticity histogram is established according to the CIE 1931 system. The cluster center is automatically generated based on the distribution of pixels in the coordinate system. Euclidean distances in the chroma space are calculated one by one between pixels and each cluster center. These pixels are clustered around the
nearest clustering center. Experimental results show that the proposed clustering algorithm can accurately extract the dominant color, and automatically segment the color image. In addition, the algorithm uses less time in image segmentation than common algorithms.