Image segmentation based on adaptive mixture model

As an important research field, image segmentation has attracted considerable attention. The classical geodesic active contour (GAC) model tends to produce fake edges in smooth regions, while the Chan-Vese (CV) model cannot effectively detect images with holes and obtain the precise boundary. To address the above issues, this paper proposes an adaptive mixture model synthesizing the GAC model and the CV model by a weight function. According to image characteristics, the proposed model can adaptively adjust the weight function. In this way, the model exploits the advantages of the GAC model in regions with rich textures or edges, while exploiting the advantages of the CV model in smooth local regions. Moreover, the proposed model is extended to vector-valued images. Through experiments, it is verified that the proposed model obtains better results than the traditional models.