An efficient automatic hierarchical image segmentation algorithm based on Modal Analysis and Mutational Agglomeration

In this paper, we propose a novel robust unsupervised image content understanding approach that segments an image into its constituent parts automatically. The aim of this algorithm is to produce precise segmentation of images using intensity information along with neighbourhood relationships. Here, automatic hierarchical discovery of classes or clusters in images takes place rather than generating the class or cluster descriptions from training image sets. In addition, cluster ensemble has been utilized for introducing a robust technique for finding the number of components in an image automatically. The experimental results reveal that the proposed method is able to find the accurate number of objects or components in an image and eventually is capable of producing more accurate and faithful segmentation. This approach utilizes a threshold to capture the relationships among the neighbouring pixels and integrate that information into the Modal Analysis and Mutational Agglomeration (MAMA) model. This proposed algorithm is very simple in implementation, fast in encoding time. Experimental results show that the algorithm generates good quality segmented image. Finally, we have compared our results with another well-known segmentation approach.