Color image segmentation by partitional clustering algorithms

This paper presents the results of some partitional clustering algorithms applied to the segmentation of color images in the RGB space. As more information is involved in the algorithm, and the distance measure is more flexible, the better the results. The selected algorithms for this work are the K-means, the FCM, the GK-B, and the GKPFCM. The GKPFCM gives the better results when all the algorithms are applied to the segmentation of two images, an image of bananas and the other one of tomates at different stages of ripeness in both cases. The results are interesting as it is possible to identify the objects, to determine the degree of ripeness, and to estimate the amount and proportion of ripe objects for a possible decision-making.

I. Introduction

In this paper we focus on the segmentation of images, a low-level operation and one of the most difficult but key in the digital processing of an image [1], because it is a first step to implement high-level operations, such as pattern recognition, computer vision, among others. Segmentation involves the detection, using deterministic or stochastic procedures, of the contours or regions that are in the image, that is, it represents the objects present and based on the intensity of the pixels and/or spatial information of these ones. Thus, the pixels that belong to the same region should have a similar intensity and to be spatially connected. This has given rise to a long list of techniques for image processing applications in the field of artificial vision.