Abstract: In the most recent years, content based image retrieval has been studied with more attention due to huge amounts of image data accumulate in different fields, e.g., medical, satellite, art collections, commercial images and general photographs. Image databases are generally big, and in most of the cases, the images are indexed only by keywords given by a human. Even though keywords are most useful in retrieving images that a user wants, sometimes the keyword approach is not sufficient and not efficient. Instead, a Query-by-example (QBR) or pictorial-query approach gives similar images to the query image given by a user. The query images can be a photograph, user-painted example, or line- drawing sketch. In this method, images are retrieved by their contents such as color, texture, shape, or objects. Thus, the degree of similarity between query images and images in database can be measured by color feature extraction, texture feature extraction, shape feature extraction similarity, or object presence between the two images. Using a single feature extraction for the image retrieval cannot be a amicable solution for the accuracy and efficiency. High-dimensional feature will reduce the query efficiency; low-dimensional feature will reduce the query accuracy, so that, better way is using multi features for image retrieval. Color, texture and shape are the most important visual features.
Keywords: Content Based Image Retrieval(CBIR), Hue(H), Saturation(S), Value (V), Grey Level Co-Occurrence Matrix (GLCM).