A quick and accurate extraction of dominant colors of background images is the basis of adaptive camouflage design. This paper proposes a Color Image Quick Fuzzy C-Means (CIQFCM) clustering algorithm based on clustering spatial mapping. First, the clustering sample space was mapped from the image pixels to the quantized color space, and several methods were adopted to compress the amount of clustering samples. Then, an improved pedigree clustering algorithm was applied to obtain the initial class centers. Finally, CIQFCM clustering algorithm was used for quick extraction of dominant colors of background image. After theoretical analysis of the effect and efficiency of the CIQFCM algorithm, several experiments were carried out to discuss the selection of proper quantization intervals and to verify the effect and efficiency of the CIQFCM algorithm. The results indicated that the value of quantization intervals should be set to 4, and the proposed algorithm could improve the clustering efficiency while maintaining the clustering effect. In addition, as the image size increased from 128 × 128 to 1024 × 1024, the efficiency improvement of CIQFCM algorithm was increased from 6.44 times to 36.42 times, which demonstrated the significant advantage of CIQFCM algorithm in dominant colors extraction of large-size images.

快速准确地提取背景图像的主色是自适应伪装设计的基础。本文提出了一种基于聚类空间映射的彩色图像快速模糊C-均值(CIQFCM)聚类算法。首先将聚类样本空间从图像像素映射到量化的色彩空间，并采用多种方法压缩聚类样本的数量。然后，应用改进的谱系聚类算法来获得初始类中心。最后利用CIQFCM聚类算法快速提取背景图像的主色。在对CIQFCM算法的效果和效率进行理论分析后，通过多次实验讨论选择合适的量化区间并验证CIQFCM算法的效果和效率。结果表明，量化间隔的取值应设为4，所提算法在保持聚类效果的同时提高了聚类效率。此外，随着图像尺寸从128×128增加到1024×1024，CIQFCM算法的效率提升从6.44倍提高到36.42倍，体现了CIQFCM算法在大尺寸图像主色提取方面的显着优势。