Clustering, classification and image segmentation on the grid

Abstract

In this short note we focus on clustering and classification problems related to image segmentation. Our aim is twofold. First we want to popularize some methods from computational geometry in grid clustering and classification, and second, we develop new techniques and present an efficient algorithm for the image segmentation on the bounded domain. Our point of view is the simplicity and time efficiency. Our reasonings are also supported by new underlying statistical model which makes a non-standard use of Markov random field in the space of spectral signatures.