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Proceedings Paper

Evaluating content-based image retrieval techniques using perceptually based metrics
Author(s): Janet S. Payne; Lee Hepplewhite; T. John Stonham
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Paper Abstract

Content-based Image Retrieval is an area of growing interest. Various approaches exist which use color, texture, and shape for retrieving 'similar' images from a database. However, what do we mean by 'similar'. Traditionally, similarity is interpreted as distance in feature space. But this does not necessarily match the human users' expectations. We report on two human studies, which asked volunteers to select which imags they considered to be 'most like' each image from the Brodatz dataset. Although the images from the Brodatz set have the advantage of being an agreed standard in texture analysis, Brodatz certainly did not select his images with this in mind. The results from this study provide a justification for selecting a subset of the Brodatz data set for use in evaluating texture-based retrieval techniques. Images which humans have difficulty in agreeing which other images are 'most like' are also poor choices for comparison. Our result indicate which images are most likely to be classified as 'similar' by individual humans and that can also serve to evaluate computer-based retrieval techniques.

Paper Details

Date Published: 9 March 1999
PDF: 12 pages
Proc. SPIE 3647, Applications of Artificial Neural Networks in Image Processing IV, (9 March 1999); doi: 10.1117/12.341112
Show Author Affiliations
Janet S. Payne, Buckinghamshire Chilterns Univ. College (United Kingdom)
Lee Hepplewhite, Brunel Univ. (United Kingdom)
T. John Stonham, Brunel Univ. (United Kingdom)


Published in SPIE Proceedings Vol. 3647:
Applications of Artificial Neural Networks in Image Processing IV
Nasser M. Nasrabadi; Aggelos K. Katsaggelos, Editor(s)

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