Image Segmentation Method

Image Segmentation Based on Multiple Means Using Class Division Method

Abstract— Image segmentation is the process of partitioning a digital image into multiple segments. Image segmentation is typically used to locate objects and boundaries (points, lines, curves, etc.) in images. Segmentation is an important part of many automated image recognition systems. The goal of segmentation is to simplify and/or change the representation of an image into units that are easier to analyze. It is the process of assigning a label to every pixel in an image such that pixels with the same label share certain visual characteristics. A histogram based image segmentation method is presented which divides the whole image into sub regions based on two

Some of the features are intensity of the pixels, boundary of the objects in the images and color. Segmentation is a preprocessing method for isolating objects, which can be used further for processing. The level to which subdivision is to be carried out depends on the problem being solved. Two approaches to segmentation are edge based [1] – [3] and region based [4] – [6] techniques. Edge based techniques make use of the property of discontinuity whereas region based methods exploit the property of similarity. Numerous algorithms using these approaches have been proposed for image segmentation. Another classification for segmentation techniques are based on contextual or non-contextual information. Non-contextual methods take no account of spatial relationships between features in an image and group pixels together on the basis of some global attribute, e.g. grey level or colour. Contextual techniques additionally exploit these

Using the discriminant criterion, an optimal threshold was selected, so as to maximize the separability of the resultant classes in gray levels. The procedure used only the zeroth and the first-order cumulative moments of the gray-level histogram. The method can be extended to multithreshold problems. Experimental results showing good values were presented to support the validity of the method.
In [9], an interval type 2 (IT2) fuzzy entropy based approach is used to compute optimum thresholds for multistage gray scale image segmentation. By finding the maximum IT2 fuzzy entropy of the gray scale image, the optimum thresholds are computed. A termination criterion for multistage segmentation is also proposed based on the range of computed entropy values of the images. Several experimental results are shown to demonstrate the performance of our proposed algorithm which outperforms type 1 (T1) fuzzy based methods.
In [10], a method for relaxation of the ultrafuzziness measurement by considering ultrafuzziness for background and object fuzzy sets separately, was proposed. The method optimizes ultrafuzziness to decrease uncertainty in fuzzy system used type II fuzzy sets. Experimental results on several images showed the effectiveness of the method.

SOLUTION