Unsupervised image segmentation by identifying natural clusters

Object-based classification is a rapidly developing paradigm in image analysis. Unlike pixel-based techniques which only use the layer values, the object-based techniques can also use shape and context information of a scene texture, thereby offering more degrees of freedom in image analysis. The first and important step of an object-based classification system is the segmentation of the image in to primitive objects. Various segmentation algorithms have already been developed to serve this purpose each of them having its own set of limitations. In this article, we present a new algorithm for image segmentation based on identifying natural clusters. This new algorithm is not the perfect solution to handle various segmentation problems. However, in some cases it is proven to be efficient.