I.J. Image, Graphics and Signal Processing, 2012, 1, 28-34 Published Online February 2012 in MECS (http://www.mecs-press.org/) DOI: 10.5815/ijigsp.2012.01.04 Color Thresholding Method for Image Segmentation of Natural Images Nilima Kulkarni New Horizon College of Engineering, Bangalore, India e-mail: kulkarninilima@gmail.com Abstract—Most of the thresholding procedures involved setting of boundaries based on grey values or intensities of image pixels. In this paper, the thresholding is to be done based on color values in natural images. The color thresholding technique is being carried out based on the adaptation and slight modification of the grey level thresholding algorithm. Multilevel thresholding has been conducted to the RGB color information of the object extract it from the background and other objects. Different natural images have been used in the study of color information. The results showed that by using the selected threshold values, the image segmentation technique has been able to separate the object from the background. Index Terms—Color image segmentation, Color thresholding, Multilevel thresholding, Natural images, RGB color information. I. INTRODUCTION Segmentation process subdivides an image into its constituent regions or objects. The level of subdivision depends on the problem being solved, where the segmentation should stop when the objects of interest in an application have been isolated. Image segmentation refers to partitioning of an image into different regions that are homogeneous or “similar” in some image characteristics. It is usually the first task of any image analysis process module and thus, subsequent tasks rely strongly on the quality of segmentation [10]. Various techniques have been proposed in the literature where color, edges, and texture were used as properties for segmentation. Using these properties, images can be analyzed for use in several applications including video surveillance, image retrieval, medical imaging analysis, and object classification. On the outset, segmentation algorithms were implemented using grayscale information only [2]. The advancement in color technology facilitated the achievement of meaningful segmentation of images as described in [3, 4]. The use of color information can significantly improve discrimination and recognition capability over gray-level methods. However, early procedures consisted of clustering pixels by utilizing only color similarity. Spatial locations and correlations of pixels were not taken into account yielding, fragmented regions throughout the image. Statistical methods, such as Classical Bayes decision theory, which are based on previous observation, have also been quite popular [5, 6]. However, these methods depend on global a priori knowledge about the image content and organization. Until recently, very little work had used underlying physical models of the color image formation process in developing color difference metrics. Because the human eyes have adjustability for the brightness, which we can only identified dozens of Gray-scale at any point of complex image, but can identify thousands of colors. In many cases, only utilize gray-Level information cannot extract the target from background; we must by means of color information. Accordingly, with the rapidly improvement of computer processing capabilities, the color image processing is being more and more concerned by people [25, 31]. The color image segmentation is also widely used in many multimedia applications, for example; in order to effectively scan large numbers of images and video data in digital libraries, they all need to be compiled directory, sorting and storage, the color and texture are two most important features of information retrieval based on its content in the images and video. Therefore, the color and texture segmentation often used for indexing and management of data; another example of multimedia applications is the dissemination of information in the network [26]. Today, a large number of multimedia data streams sent on the Internet, However, due to the bandwidth limitations; we need to compress the data, and therefore it calls for image and video segmentation. Human eyes can distinguish thousands of colors but can only distinguish 20 kinds of grayscale, so we can easily and accurately find the target from the color images. However, it is difficult to find out from the gray-scale image. The reason is that color can provide more information than grayscale. The color for the pattern recognition and machine vision is very useful and necessary [27]. At present, specifically applied to the color image segmentation approach is not so much as for the gray-scale images, most of proposed color image segmentation methods are the combination of the existing grayscale image segmentation method on the basis of different color space. Commonly used for color image segmentation methods are histogram threshold, feature space clustering, region-based approach, based on edge detection methods, fuzzy methods, artificial Copyright © 2012 MECS I.J. Image, Graphics and Signal Processing, 2012, 1, 28-34