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Proceedings Paper

Learning a similarity-based distance measure for image database organization from human partitionings of an image set
Author(s): David McG. Squire
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Paper Abstract

In this paper our goal is to employ human judgments of image similarity to improve the organization of an image database for content-based retrieval. We first derive a statistic, KB, for measuring the agreement between two partitionings of an image set into unlabeled subsets. This measure can be used to measure both the degree of agreement between pairs of human subjects and that between human and machine partitionings of an image set. It also allows a direct comparison of database organizations, as opposed to the indirect measure available via precision and recall measurements. This provides a rigorous means of selecting between competing image database organization systems, and assessing how close the performance of such systems is to that which might be expected from a database organization done by hand.

Paper Details

Date Published: 5 October 1998
PDF: 9 pages
Proc. SPIE 3527, Multimedia Storage and Archiving Systems III, (5 October 1998); doi: 10.1117/12.325854
Show Author Affiliations
David McG. Squire, Univ. of Geneva (Australia)

Published in SPIE Proceedings Vol. 3527:
Multimedia Storage and Archiving Systems III
C.-C. Jay Kuo; Shih-Fu Chang; Sethuraman Panchanathan, Editor(s)

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