1 Feature Sets Based Similarity Measures for Image Retrieval ASIS&T Annual Meeting, Charlotte, NC October 28 – November 2, 2005 Abebe Rorissa ([email protected]) College of Computing and Information, University at Albany, State University of New York (SUNY)
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Feature Sets Based Similarity Measures for Image Retrieval
Feature Sets Based Similarity Measures for Image Retrieval. Abebe Rorissa. ([email protected]) College of Computing and Information, University at Albany, State University of New York (SUNY). ASIS&T Annual Meeting, Charlotte, NC October 28 – November 2, 2005. Outline. Background - PowerPoint PPT Presentation
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Feature Sets Based Similarity Measures for Image Retrieval
ASIS&T Annual Meeting, Charlotte, NCOctober 28 – November 2, 2005
Feature Sets based Similarity Measures – Generalized Contrast
Model
A)-f(BB)-f(AB)f(AB)f(A
b)S(a,
Where & are non-negative (≥ 0)
The values of S(a,b) range from 0 to 1
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Summary & Concluding Remarks
Similarity matching/measure is a key component of information retrievalA similarity measure based on the Contrast Model offers an alternativeIt gives more weight to common features than distinctive featuresIt takes into account any definition of a feature set and could be applied to any type of document (text, 2D, 3D, moving, etc.)It matches perceptual similarity, which is important if we are to bridge the gap between IR systems and users
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Looking Ahead
Image organization and retrieval research not coordinated
Need for a common test collection and a retrieval evaluation conference along the lines of the TREC video track
ASIS&T is an appropriate forum
Current initiatives http://ir.shef.ac.uk/imageclef/
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References
Rubner, Y. (1999). Perceptual metrics for image database navigation. Unpublished doctoral dissertation, Stanford University, Stanford, CA.
Tversky, A. (1977). Features of similarity. Psychological Review, 84(4), 327-352.
Zachary, J., & Iyengar, S.S. (2001). Information theoretic similarity measures for content based image retrieval. Journal of the American Society for Information Science and Technology, 52, 856-867.