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Proceedings Paper

Image retrieval using image context vectors
Author(s): Steve Gallant; David M. Fram
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Paper Abstract

Searching image databases using image queries is a challenging problem. For the analogous problem with text, those document retrieval methods that use `superficial' information, such as word count statistics, generally outperform natural language understanding approaches. This motivates an exploration of `superficial' feature-based methods for image retrieval. The main strategy is to avoid full image understanding, or even segmentation. The key question for any image retrieval approach is how to represent the images. We are exploring a new image context vector representation. A context vector is a high (approximately 300) dimensional vector that can represent images, sub-images, or image queries. The image is first represented as a collection of pairs of features with relative orientations defined by the feature pairs. Each feature pair is transformed into a context vector, and then all the vectors for pairs are added together to form the 300-dimensional image context vector for the entire image. This paper examines the image context vector approach and its expected strengths and weaknesses.

Paper Details

Date Published: 31 January 1995
PDF: 11 pages
Proc. SPIE 2368, 23rd AIPR Workshop: Image and Information Systems: Applications and Opportunities, (31 January 1995); doi: 10.1117/12.200783
Show Author Affiliations
Steve Gallant, Belmont Research Inc. (United States)
David M. Fram, Belmont Research Inc. (United States)


Published in SPIE Proceedings Vol. 2368:
23rd AIPR Workshop: Image and Information Systems: Applications and Opportunities
Peter J. Costianes, Editor(s)

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