Image retrieval

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An image retrieval system is a computer system for browsing, searching and retrieving images from a large database of digital images. Most traditional and common methods of image retrieval utilize some method of adding metadata such as captioning', keywords, or descriptions to the images so that retrieval can be performed over the annotation words. Manual image annotation is time-consuming, laborious and expensive; to address this, there has been a large amount of research done on automatic image annotation. Additionally, the increase in social web applications and the semantic web have inspired the development of several web-based image annotation tools.

The first microcomputer-based image database retrieval system was developed at MIT, in the 1990s, by Banireddy Prasaad, Amar Gupta, Hoo-min Toong, and Stuart Madnick.[1]

A 2008 survey article documented progresses after 2007.[2]

Search methods[edit]

Image search is a specialized data search used to find images. To search for images, a user may provide query terms such as keyword, image file/link, or click on some image, and the system will return images "similar" to the query. The similarity used for search criteria could be meta tags, color distribution in images, region/shape attributes, etc.

Data Scope[edit]

It is crucial to understand the scope and nature of image data in order to determine the complexity of image search system design. The design is also largely influenced by factors such as the diversity of user-base and expected user traffic for a search system. Along this dimension, search data can be classified into the following categories:

Evaluations[edit]

There are evaluation workshops for image retrieval systems aiming to investigate and improve the performance of such systems.

See also[edit]

References[edit]

  1. ^ Prasad, B E; A Gupta, H-M Toong, S.E. Madnick (February 1987). "A microcomputer-based image database management system". IEEE Transactions on Industrial Electronics. IE-34 (1): 83–8. doi:10.1109/TIE.1987.350929. 
  2. ^ Datta, Ritendra; Dhiraj Joshi, Jia Li, James Z. Wang (April 2008). "Image Retrieval: Ideas, Influences, and Trends of the New Age". ACM Computing Surveys 40 (2): 1–60. doi:10.1145/1348246.1348248. 
  3. ^ Jorge E. Camargo, Juan C. Caicedo, Fabio A. Gonzalez, A kernel-based framework for image collection exploration, Journal of Visual Languages & Computing, Volume 24, Issue 1, February 2013, 53-57. ISSN 10-45-926X. http://dx.doi.org/10.1016/j.jvlc.2012.10.008

External links[edit]