关键词: 苗族服饰, 图像分割, 主动轮廓, 拟合图像, Kullback-Leibler散度, 自适应权重

Abstract: Miao costume images have problems such as embroidery line texture, complex shape and large color difference. In view of these problems, an image segmentation algorithm of Miao costumes based on fuzzy fitting image is proposed. Firstly, the fuzzy local and global fitting images are defined in the image fuzzy region. Meanwhile, according to the nature of Kullback-Leibler divergence used to describe the difference between two probability distributions, the fuzzy energy function is constructed based on the image difference between the original image and the fuzzy local and global fitting images in the Kullback-Leibler divergence, which can drive the initial contour to the target boundary. Next, the adaptive weight is defined by using the normalized intra-class variances of the pixel grayscale inside and outside the contour curves of the image, which can automatically adjust the parameters between the local and global fuzzy energy terms. Finally, we add a regularization term and a length term in the energy function, and an edge detector is introduced into the regularization term and length term to smooth the edge of the image. In the experiment, the validity of the proposed algorithm is verified by experiments on natural images. The similarity and sensitivity coefficients of the segmentation results are above 0.978 and 0.981, respectively. Then, the proposed optimization algorithm is applied to the Miao costume image segmentation. The results show that the proposed algorithm has good segmentation results, and it requires a few iterations and a little segmentation time. Furthermore, the segmentation results are robust to the initial level set function and the Miao costume images.

Key words: Miao costume, image segmentation, active contour, fitting image, Kullback-Leibler divergence, adaptive weight