Learning a similarity-based distance measure for image database organization from human partitionings of an image set

David McG. Squire

doi:10.1117/12.325854

5 October 1998 Learning a similarity-based distance measure for image database organization from human partitionings of an image set

David McG. Squire

Author Affiliations +

Proceedings Volume 3527, Multimedia Storage and Archiving Systems III; (1998) https://doi.org/10.1117/12.325854
Event: Photonics East (ISAM, VVDC, IEMB), 1998, Boston, MA, United States

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, K_B, 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.

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David McG. Squire "Learning a similarity-based distance measure for image database organization from human partitionings of an image set", Proc. SPIE 3527, Multimedia Storage and Archiving Systems III, (5 October 1998); https://doi.org/10.1117/12.325854

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