Object-background segmentation using new definitions of entropy

Abstract

The definition of Shannon's entropy in the context of information theory is critically examined and some of its applications to image processing problems are reviewed. A new definition of classical entropy based on the exponential behaviour of information-gain is proposed along with its justification. Its properties also include those of Shannon's entropy. The concept is then extended to fuzzy sets for defining a non-probabilistic entropy and to grey tone image for defining its global, local and conditional entropy. Based on those definitions, three algorithms are developed for image segmentation. The superiority of these algorithms is experimentally demonstrated for a set of images having various types of histogram.