Variational and PCA based natural image segmentation ?
This paper introduces a novel variational segmentation method within the fuzzy framework, which solves the problem of segmenting multi-region color-scale images of natural scenes. We call this kind of images as natural images. The advantages of our segmentation method are: (1) by introducing the PCA descriptors, our segmentation model can partition color-texture images better than classical variational-based segmentation models, (2) to preserve geometrical structure of each fuzzy membership function, we propose a nonconvex regularization term in our model, (3) to solve our segmentation model more efficiently, we design a fast iteration algorithm in which we integrate the augmented Lagrange multiplier method and the iterative reweighting. We conduct comprehensive experiments to measure the segmentation performance of our model in terms of visual evaluation, and we also demonstrate the efficiency of the corresponding algorithm in terms of a variety of quantitative indices. The proposed model achieves better segmentation results compared with some other well-known models, such as the level-set model and the fuzzy region competition model, while the proposed algorithm is much more efficient than the algorithm of the state-of-the-art natural image segmentation model.
image segmentation iterative reweighting region competition principal component analysis variable splitting