A Texture Segmentation Algorithm Based on PCA and Global Minimization Active Contour Model for Aerial Insulator Images

In this paper, a novel texture segmentation algorithm is proposed to partition the complex aerial insulator images into sub-regions with closed smooth contours. Firstly, Gray Level Co-occurrence Matrix (GLCM) is employed to extract the texture features of insulators and is calculated by the rapid Gray Level Co-occurrence Integrated Algorithm (GLCIA). We divide the extracted texture features into two categories: one with the stronger discriminative ability and the other with weaker ability. The second category is optimized by Principal Component Analysis (PCA) to better distinguish the different texture objects with low contrast. Then, a new convex energy functional is defined by taking the non-convex model of the Texture Descriptor Active Contour (TDAC) into a global minimization framework (GMAC) during segmentation. The proposed energy functional can avoid the existence of local minima in the minimization of the TDAC. A fast dual formulation is introduced for the efficient evolution of the contour. The experimental results on synthetic and real aerial insulator remote sensing images have shown that the proposed algorithm obtains more satisfactory segmentation compared to the classical models in terms of accuracy, efficiency and independence of initial contour. The influence of the algorithm parameters is also analyzed.