11. Image Mining and Content-Based Image Retrieval

  1. Tinku Acharya and
  2. Ajoy K. Ray

Published Online: 20 SEP 2005

DOI: 10.1002/0471745790.ch11

Image Processing: Principles and Applications

Image Processing: Principles and Applications

How to Cite

Acharya, T. and Ray, A. K. (2005) Image Mining and Content-Based Image Retrieval, in Image Processing: Principles and Applications, John Wiley & Sons, Inc., Hoboken, NJ, USA. doi: 10.1002/0471745790.ch11

Publication History

  1. Published Online: 20 SEP 2005
  2. Published Print: 19 AUG 2005

ISBN Information

Print ISBN: 9780471719984

Online ISBN: 9780471745792



  • image features;
  • color;
  • shape;
  • texture;
  • topology;
  • content-based image retrieval;
  • CBIR;
  • similarity measure;
  • image mining;
  • indexing


Retrieval of a query image from a large database of images is an important task in the area of computer vision and image processing. The advent of large multimedia collections and digital libraries have led to an important requirement for development of search tools for indexing and retrieving information from them. To a very large extent, the low level image features such as color, texture and shape are widely used for content-based image retrieval (CBIR). The content-based query system processes a query image and assigns this unknown image to the closest possible image available in the database. As a result, it may be concluded that selection and extraction of low level image features constituting the image and subsequent similarity based classification are the two issues in content-based image retrieval. Often extraction of knowledge or patterns from the large image databases is called “image mining” techniques as well. CBIR can be used as a tool for extraction of such patterns. In this chapter, we have presented the principles behind content- based image retrieval and also some results.