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Proceedings Paper

Human perception of trademark images: implications for retrieval system design
Author(s): Manling Ren; John P. Eakins; Pamela Briggs
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Paper Abstract

Modeling human shape similarity judgments involves identifying perceptually significant image elements, selecting appropriate features to represent their shape, and computing suitable similarity measures. This paper is concerned with the first of these - identification of the way in which humans segment abstract trademark images. A sample of 63 trademark images was shown to several groups of students from different subject backgrounds in two experiments. Students were first presented with printed versions of a number of abstract trademark images, and invited to sketch their preferred segmentation of each image. A second group of studies was then shown each image, plus its set of alternative segmentations, and invited to rank each alternative in order of preference. The degree of agreement over how images should be segmented varied substantially form one image to another. Qualitative analysis of our result suggested that participants used a relatively small number of segmentation strategies, reflecting well-known psychological principles. Agreement between human image segmentations and those generated by our ARTISAN trademark retrieval system was quite limited, indicating that ARTISAN is currently capable of modeling only a small subset of the mechanisms used by human participants. The implications of these experiments for the future development of ARTISAN are discussed.

Paper Details

Date Published: 24 August 1999
PDF: 12 pages
Proc. SPIE 3846, Multimedia Storage and Archiving Systems IV, (24 August 1999); doi: 10.1117/12.360416
Show Author Affiliations
Manling Ren, Univ. of Northumbria at Newcastle (United Kingdom)
John P. Eakins, Univ. of Northumbria at Newcastle (United Kingdom)
Pamela Briggs, Univ. of Northumbria at Newcastle (United Kingdom)

Published in SPIE Proceedings Vol. 3846:
Multimedia Storage and Archiving Systems IV
Sethuraman Panchanathan; Shih-Fu Chang; C.-C. Jay Kuo, Editor(s)

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