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Proceedings Paper

ImageGREP: fast visual pattern matching in image databases
Author(s): David A. White; Ramesh C. Jain
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Paper Abstract

Most current image retrieval systems use holistic comparison that require a global match between images or presegmented object in images. However, often the user of an image database system is interested in a local match between images. For example, `Find images from the database with something like this anywhere in the image,' or `Fine images with something like this in some region of any image in the database,' or `Find images with this spatial configuration of regions like this.' In this paper, we provide an overview of a new framework that should help to allow these types of queries to be answered efficiently. In order to illustrate the usefulness of our framework, we have developed a complete image retrieval system based on local color information. Our system features fully automatic insertion and very efficient query execution, rivaling the efficiency of systems that can only handle global image comparisons. The query execution engine, called the ImageGREP Engine, can process queries at a speed of approximately 3000 images per second (or better) on a standard workstation when the index can be stored in main memory. In the future, we believe our framework should be used in other domains and applications, to handle queries based on texture or other material properties and perhaps domain specific image properties.

Paper Details

Date Published: 15 January 1997
PDF: 12 pages
Proc. SPIE 3022, Storage and Retrieval for Image and Video Databases V, (15 January 1997); doi: 10.1117/12.263447
Show Author Affiliations
David A. White, Univ. of California/San Diego (United States)
Ramesh C. Jain, Univ. of California/San Diego (United States)


Published in SPIE Proceedings Vol. 3022:
Storage and Retrieval for Image and Video Databases V
Ishwar K. Sethi; Ramesh C. Jain, Editor(s)

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