Owing to the widespread use of digital images

methods for efficient image access

management and retrieval become urgent requirements to image users and managers. Content-based image retrieval (CBIR) is a good solution for this problem

and has attracted increasing attention from researchers all over the world recently. In this paper

we review the beginning and various applications of CBIR firstly

and then we introduce some key techniques and algorithms for CBIR

such as methods and principals for image feature selection and representation

feature-based similarity computation

semantic features and relevance feedback. CBIR inherits some automatic techniques from traditional computer vision. However

CBIR and computer vision are very different in essence. We put forward our views on both common and distinct characters between them in the concluding section. CBIR is distinguished by the ability of on-line learning through interactive with users. Future research directions are also be presented in this section

including relevance feedback

features fusion

database technique and hierarchical ordered descriptions about the semantic content of images.