Abstract
In this paper Content-based image retrieval (CBIR) systems aim to return the most relevant images in a database, according to the user’s opinion for a given query. Due to the dynamic nature of the problem, which may change the meaning of relevance among users for a same query, these systems usually rely on an active learning process in which the system returns a small set of images (training set) and the user indicates their relevance at each iteration. Relevance feedback (RF) is an effective method for content-based image retrieval (CBIR), and it is also a feasible step to shorten the semantic gap between low-level visual feature and high-level Perception. increase in use of color image in recent years has motivated to the need of retrieval system for color image. Content Based Image Retrieval (CBIR) system is used to retrieve similar images from large image repositories based on color, texture and shape. In CBIR, the invariance to geometrical transformation is one of the most desire