Implementation of Fuzzy C-Means (FCM) Clustering Based Camouflage Image Generation Algorithm

Camouflage plays a fundamental role in modern electrical confrontation. Two important elements of camouflage are camouflage colors and camouflage textures. Many methods were presented to extract the main colors of the background. However, there are few methods to extract the background textures at present. The traditional methods based on watershed segmentation or background contour segmentation are computationally complex and time-consuming, being difficult to meet the real-time requirements. In this paper, a camouflage generation algorithm based on rectangle blocks scrambling and Fuzzy C-Means (FCM) clustering method is proposed. The algorithm consists of three modules, namely (1) the rectangle blocks segmentation module, (2) the rectangle scrambling module, and (3) the extraction of background dominant colors. Firstly, the texture features of the background image are simulated by rectangle blocks segmentation and scrambling algorithm, which avoids the complex calculation process and the loss of textures information compared with traditional algorithms based on description operators for background textures extraction. Next, Fuzzy C-Means (FCM) method is used to extract the main colors of background image with high accuracy and fast speed. In addition, experiments show that the proposed algorithm reduces the computing time and presents better concealment effect by retaining the similar domain colors. Compared with the template traversal algorithm and the watershed segmentation algorithm, the proposed algorithm features reduced computing time by more than 50%, and an increased similarity between the generated texture and background texture to more than 90%.