Color Image Segmentation Based on Bayes Classification and Simple Linear Iterative Clustering

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Segmentation may be a one among the interesting area of analysis for image process. Images are thought of as most significant medium of transference data. to know the image, to extract  and use of that information for alternative tasks is a very important facet of machine learning. One amongst the necessary steps in direction of understanding the image is to section them. It’s the method of dividing the image into uniform region with relation to sure options & that hopefully correspond to real objects in actual scene. Image segmentation is that the foundation method within which we divide the image into disjoint regions that are meaning. The goal is to cluster pixels into regions comparable to individual surfaces, object natural elements of object. We tend to divide the complete image into multiple segments that area unit set of pixels, pixels in a very region are like one another in some criteria, therefore on find & determine objects and corresponding boundaries in an image. In segmentation, value is appointed to each constituent in a picture specified constituent with identical value share sure characteristics in a very explicit region. 

As the image object segmentation is an tough and important requirement in image processing area of search. This paper discuss and evaluate main image segmentation techniques used for the purpose of image analysis. It is found that there is no perfect method for image segmentation because the result of image segmentation is depends on many factors, i.e., pixel color, texture, intensity, similarity of images, image content, and problem domain. Therefore, it is not possible to consider a single method for all type of images nor all methods can perform well for a particular type of image. Hence, it is good to use hybrid solution consists of multiple methods for image segmentation problem.

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