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1 Feature Sets Based Similarity Measures for Image Retrieval ASIS&T Annual Meeting, Charlotte, NC October 28 – November 2, 2005 Abebe Rorissa (arorissa@albany.edu) College of Computing and Information, University at Albany, State University of New York (SUNY)

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2 Outline Background Similarity and similarity measures in information retrieval Feature sets based similarity measures Boolean & Extended Boolean Fuzzy sets model A similarity measure based on Tverskys Contrast Model Concluding remarks

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3 Background Similarity is a construct that plays significant roles in: Cognitive psychology Linguistics & language studies Sociology & anthropology Ecology & biology Library & information science

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4 Background: Similarity Measures and Information Retrieval

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5 Background: Similarity Measures and Image Retrieval Zachary & Iyengar (2001)

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6 Background: Similarity Measures based on Geometric models The general form of the distance (called the Minkowski metric) between two points representing stimuli a and b on an n-dimensional space is given by: Where a i and b i are corresponding coordinates of stimuli a and b on the i th dimension and r is a parameter that determines the type of metric

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7 Background: Similarity Measures and Image Retrieval Rubner (1999)

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8 Background: Geometric models and Metric Space Dim 1 Dim 2 Symmetry: D(a,b)=D(b,a)???? Triangle inequality: D(a,b)+ D(b,c) D(a,c)???? a b c

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9 Background: The Contrast Model A-BB-A A B A B S(a,b)= f(A B) - f(A-B) - f(B-A) Distinctive features of a Distinctive features of b Common features of a & b

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10 Background: The Contrast Model Results of a Study Independent VariableBBetat Common Features (method1)1.196.4869.505** Distinctive Features of a (method1)-.033-.197-4.311** Distinctive Features of b (method1)-.016-.090-2.181* R 2 =.45, F(3,431)=116.101** Common Features (method2).252.46911.136** Distinctive Features of a (method2)-.042-.163-3.865** Distinctive Features of b (method2)-.020-.089-2.210* R 2 =.30, F(3,431)=60.423** Note. *p <.05, **p <.01 R 2 =.55

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11 Feature Sets based Similarity Measures Applied in the following retrieval models: Boolean model Extended Boolean model Fuzzy sets model

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12 Feature Sets based Similarity Measures – Generalized Contrast Model Where & are non-negative ( 0) The values of S(a,b) range from 0 to 1

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13 Summary & Concluding Remarks Similarity matching/measure is a key component of information retrieval A similarity measure based on the Contrast Model offers an alternative It gives more weight to common features than distinctive features It 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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14 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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15 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.

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16 Thank You! Questions?

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1-NN Rule: Given an unknown sample X decide if for That is, assign X to category if the closest neighbor of X is from category i.

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