In this paper, we are concerned with unsupervised natural image matting. Due to the under-constrained nature of the problem, image matting algorithms are usually provided with user interactions, such as scribbles or trimaps. This is a very tedious task and may even become impractical for some applications. For unsupervised matte calculation, we can either adopt a technique that supports an unsupervised mode for alpha map calculation, or we may automate the process of acquiring user interactions provided for a matting algorithm. Our proposed technique contributes to both approaches and is based on spectral matting. The latter is the only technique in the literature that supports automatic matting but it suffers from critical limitations among which is the unreliable unsupervised operation. Stressing on that drawback, spectral matting may produce erroneous mattes in the absence of guiding scribbles or trimaps. Using the Gestalt laws of grouping, we propose a method that automatically produces more truthful mattes than spectral matting. In addition, it can be used to generate trimaps, eliminating the required user interactions and making it possible to harness the powers of matting techniques that are better than spectral matting but don't support unsupervised operation. The main contribution of this research is the introduction of the Gestalt laws of grouping to the matting problem.