Segmentation is the process of subdivision of an image into, usually, nonoverlapping regions. The pixels within a region are required to possess some specified properties of homogeneity or similarity. Segmentation techniques can be classified according to different criteria. The typical classification is to divide segmentation algorithms as follows:

• pixel-based algorithms or histogram-based algorithms if individual pixel values form the only information used to perform segmentation;

• edge-based algorithms when segmentation is based on the detection of the edges present within the given image; and

• region-based algorithms when both pixel values and the surrounding information are utilized to form different regions.

In this chapter, we shall study several segmentation techniques for application to color images based upon the approaches listed above.

5.1 Histogram-based Thresholding

The histogram of an image is a graph whose axes are the possible pixel values and the frequency of occurrence of each pixel value; see Sections 1.3 and 4.5 for discussions on and illustrations of histograms. Typically, a histogram is composed of modes, with each mode representing a meaningful object region. An image with a nearly uniform intensity has a histogram with a single mode. An image with a single object or several objects of similar values within a narrow range of intensities, placed against a background with a nearly uniform intensity of a different value, has a histogram with two modes; such a histogram is known as a bimodal histogram. Real-life images, however, have several objects comprising multiple values on varied backgrounds; the histograms of such images will be multimodal [6]. Furthermore, the ranges of values of multiple, spatially separated, and distinct objects may overlap; such a situation makes it difficult to analyze and interpret a histogram.