Background dominant colors extraction method based on color image quick fuzzy c-means clustering algorithm

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.     

基于组合赋权和直觉模糊集的合成营作战能力评估

针对评估指标权重未知、各时段权重信息已知的合成营动态作战能力评估问题.从作战要素的角度建立评估指标体系,利用组合赋权法获得评估指标的权重信息,最后在传统VIKOR方法思想的基础上,结合直觉模糊数的计算法则,利用直觉模糊加权平均算子(IFWA)对多个时段的评估结果进行集成,进而对每个合成营的作战能力进行评估.其结果与利用TOPSIS方法获得的结果进行比较,验证了该模型的可行性,为合成营作战能力评估提供一种新的思路.