We propose an automatic approach to soft color segmentation, which produces soft color segments with an appropriate amount of overlapping and transparency essential to synthesizing natural images for a wide range of image-based applications. Although many state-of-the-art and complex techniques are excellent at partitioning an input image to facilitate deriving a semantic description of the scene, to achieve seamless image synthesis, we advocate a segmentation approach designed to maintain spatial and color coherence among soft segments while preserving discontinuities by assigning to each pixel a set of soft labels corresponding to their respective color distributions. We optimize a global objective function, which simultaneously exploits the reliability given by global color statistics and flexibility of local image compositing, leading to an image model where the global color statistics of an image is represented by a Gaussian Mixture Model (GMM), whereas the color of a pixel is explained by a local color mixture model where the weights are defined by the soft labels to the elements of the converged GMM. Transparency is naturally introduced in our probabilistic framework, which infers an optimal mixture of colors at an image pixel. To adequately consider global and local information in the same framework, an alternating optimization scheme is proposed to iteratively solve for the global and local model parameters. Our method is fully automatic and is shown to converge to a good optimal solution. We perform extensive evaluation and comparison and demonstrate that our method achieves good image synthesis results for image-based applications such as image matting, color transfer, image deblurring, and image colorization.