Regions-of-Interest and Spatial Layout for Content-Based Image Retrieval

Authors:
Baback Moghaddam;Henning Biermann;Dimitris Margaritis
Affiliations:
Mitsubishi Electric Research Laboratory, 201 Broadway, Cambridge, MA 02139, USA. baback@merl.com;Department of Computer Science, Courant Institute of Mathematical Sciences, 719 Broadway, RM 1206, New York, NY 10013, USA. biermann@cs.nyu.edu;Department of Computer Science, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA. D.margaritis@cs.cmu.edu
Venue:
Multimedia Tools and Applications
Year:
2001

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Abstract

To date most “content-based image retrieval” (CBIR) techniques rely on global attributes such as color or texture histograms which tend to ignore the spatial composition of the image. In this paper, we present an alternative image retrieval system based on the principle that it is the user who is most qualified to specify the query “content” and not the computer. With our system, the user can select multiple “regions-of-interest” and can specify the relevance of their spatial layout in the retrieval process. We also derive similarity bounds on histogram distances for pruning the database search. This experimental system was found to be superior to global indexing techniques as measured by statistical sampling of multiple users' “satisfaction” ratings.