Image Segmentation Using Graph Partition Methods

Abstract

Image segmentation is a method of partitioning an image into meaningful parts, which can be put into a detailed study. This method is largely useful for the practical applications such as machine vision, bacterial study, face detection, video surveillance, fingerprint detection, etc. Graph partitioning is a method developed from Graph Theory which has multiple applications in various fields. This process considers an image as a graph with V vertices and E edges. It can be partitioned into k-components with specific properties. This enables the detailed study of an image for various purposes. The variation in the pixels, texture, etc, from one component to another component can be easily identified. This paper involves in proposing an effective Graph partitioning method overcoming all the existing disadvantages