EF-Index: Determining number of clusters (K) to estimate number of segments (S) in an image

Abstract

Estimation of number of segments in an image attracts a formidable interest among the research community. The number of segments in an image is estimated by calculating the number of clusters present in the pixels of that image. The present work offers an unsupervised method, named “Electrostatic Force Index (EF-Index)”, to estimate the number of clusters inherent in an image, reporting of which is very rare in literature. The proposed approach is inspired by Coulomb's law of electrostatics. The EF-Index explores the mutual influence of an arbitrary pixel on another, by considering them similar to point charges. Our proposed cluster indexing method, viz. EF-Index is capable of determining the number of clusters present in an image. It has a strong resemblance to the way the electrostatic force is operative between a pair of static point charges in a closed system as per Coulomb's principle. In order to justify the effectiveness of the proposed approach, we have compared EF-Index of a given image with DB-Index, I-Index, CVNN-Index, DOE-AND-SCA and Sym-Index of the same image. Experimental results show that EF-Index is same as other state-of-the-art indices, whereas EF-Index does not require any clustering algorithm. To establish the applicability of the EF-Index, the same is applied for image segmentation considering Berkeley Segmentation Dataset and Stanford Background Dataset. We observe the results obtained conform to the ground truth and results achieved by applying existing well-established segmentation techniques on the same datasets. The efficacy of the proposed approach is further substantiated in terms of its reduced computational overhead in comparison to the state-of-the-art segmentation algorithms.