Pixel1.gif (51 bytes)
Pixel1.gif (51 bytes)
Pixel1.gif (51 bytes) Main Page Pixel1.gif (51 bytes)
About DSP Laboratory
People
Research
Publications
Courses
Pixel.gif (52 bytes)
Contact Us
Sponsors
Credits
Pixel.gif (52 bytes)
Search
Go to FIU's Homepage

 

 Pixel1.gif (51 bytes)

 

Curve.gif (104 bytes) Pixel1.gif (51 bytes)

Content-Based Image Retrieval

Pixel1.gif (51 bytes)

Abstract:
 
"Content-Based Image Retrieval", (2007)
Adjouadi, M., Lucas, M., Pozo, E., Nguyen H., Maynard K., Thomas S., Barreto, A., Graham, S., and Rishe, N.

ABSTRACT: With today's large increase in digital images and automatically generated imagery, such as videos and stills generated from surveillance equipment, the need for efficient image retrieval and indexing has become fundamental. Since text-based information retrieval has been shown to perform very poorly when searching through images, research has been active in the field of content-based image retrieval (CBIR). CBIR systems make use of the properties of images in order to compare them and extract content by matching the query image. Comparing features - such as color, texture, and shape - allows for better retrieval accuracy; however, the algorithms used are still very limited.

This paper will provide a survey of CBIR systems and explain the fundamental properties and techniques used in these systems. First, the history of CBIR systems will be discussed together with some typical CBIR systems. After this, the paper will touch on text-based information retrieval and explain why it does not work for searching through collections of images. The latter portion of this document will provides an overview of a typical CBIR system and the main techniques involved in querying such a system. Finally, image features and indexing schemes will be described.