Color spaces advantages and disadvantages in image color clustering segmentation

Abstract

Machine learning has been widely used in image analysis and processing for the purpose of letting the computer recognize specifics aspects like color, shape, textures, size, and position. Such procedures allow to have algorithms capable of identifying objects, find relationships, and perform tracking. The present chapter executes an analysis of the one image attribute that is listed among the basic aspects of an image and the color. The color is chosen due to the importance given by humans to this attribute. Also the color is used main filter criteria in object recognition and tracking. In this section the effect of the color is studied from the point of view of the most popular color spaces available in image processing. It will be tested the effect of the selection of a given color space one of the most common clustering machine learning algorithms. The chosen algorithm is K-means ++, a variation of the popular K-means, which allows a more fair evaluation since it mitigates some of the randomness in the final cluster configuration. Every advantage or weakness will be exposed, so it can be known what color spaces are the right choice depending on the desired objective in the image processing.