Color-based Relevance Feedback Image Retrieval System

Recent years have seen a rapid increase of the size of digital image collections. Everyday, both military and civilian equipment generates giga-bytes of images. Huge amount of information is out there. However, we can not access to or make use of the information unless it is organized so as to allow efficient browsing, searching and retrieval, Image Retrieval has been a very active research area since the 1970's, with the thrust from two major research communities, Database Management and Computer Vision. These two research communities study Image Retrieval from different angles, one being text-based and the other visual-based. The text-based Image Retrieval can be traced back to the late 1970's. A very popular framework of Image Retrieval contains Data Modeling, Multi-Dimensional Indexing, Query Evaluation, etc.. However, there exist two major difficulties, especially when the size of image collections is large (tens or hundreds of thousands). One is the vast amount of labor required in manual image annotation. The other difficulty, which is more essential, results from the rich content in the images and the subjectivity of human perception. That is, for the same image content, different people may perceive it differently. The perception subjectivity and annotation impreciseness may cause unrecoverable mismatches in the later retrieval processes. In the early 90's, because of the emergence of large-scale image collections, the two difficulties faced by the manual annotation approach became more and more acute. To overcome these difficulties, content-based Image Retrieval (CBIR) was proposed. That is, instead of being manually annotated by text-based keywords, images would be indexed by their own visual content, such as Color, Texture, Shape, etc. Since then, many techniques in this research direction have been developed and many Image Retrieval systems, both research and commercial, have been built. The advances in this research direction are mainly contributed by the Computer Vision community. This approach has established a general framework of Image Retrieval from a new perspective. However, there are still many open research issues to be solved before such retrieval systems can be put into practice. Regarding Content-Based Image Retrieval, we feel there is a need to survey what has been achieved in the past few years and what are the potential research directions which can lead to compelling applications. In this paper we will devote our effort primarily to the Content-based Image Retrieval paradigm. There are four fundamental bases for Content-Based Image Retrieval, i.e. Visual Feature Extraction, Multi-Dimensional Indexing, Retrieval System Design and Relevance Feedback. I review various visual features and their corresponding representation and matching techniques. To facilitate fast search in large-scale image collections, effective indexing techniques need to be explored. Ie realized the CBIR based on Color features with Relevance Feedback and given some conclusion and discuss. The CBIR system runs ~vell with a large image library.