The importance of an effective technique in searching and retrieving images from the huge collection cannot be overemphasized. One approach for indexing and retrieving image data is using manual text annotations. The annotations can then be used to search images indirectly. But there are several problems with this approach. First, it is very difficult to describe the contents of an image using only a few keywords. Second, the manual annotation process is very subjective, ambiguous, and incomplete. Those problems have created great demands for automatic and effective techniques for content-based image retrieval (CBIR) systems. Most CBIR systems use low-level image features such as color, texture, shape, edge, etc., for image indexing and retrieval. It’s because the low-level features can be computed automatically. Content Based Image Retrieval (CBIR) has emerged during the last several years as a powerful tool to efficiently retrieve images visually similar to a query image. The main idea is to represent each image as a feature vector and to measure the similarity between images with distance between their corresponding feature vectors according to some metric. Finding the correct features to represent images with, as well as the similarity metric that groups visually similar images together, are important steps in the construction of any CBIR system.