Comparison of the effectiveness of alternative feature sets in shape retrieval of multicomponent images

Eakins, John P., Edwards, Jonathan D., Riley, K. Jonathan and Rosin, Paul L. ORCID: https://orcid.org/0000-0002-4965-3884 2001. Comparison of the effectiveness of alternative feature sets in shape retrieval of multicomponent images. Presented at: Storage and Retrieval for Media Databases 2001, San Jose, CA, USA, 24-26 January 2001. Published in: Yeung, Minerva M., Li, Chung-Sheng and Lienhart, Rainer W. eds. Storage and Retrieval for Media Databases 2001. Proceedings of SPIE (4315) The International Society for Optical Engineering, p. 196. 10.1117/12.410929

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Abstract

Many different kinds of features have been used as the basis for shape retrieval from image databases. This paper investigates the relative effectiveness of several types of global shape feature, both singly and in combination. The features compared include well-established descriptors such as Fourier coefficients and moment invariants, as well as recently-proposed measures of triangularity and ellipticity. Experiments were conducted within the framework of the ARTISAN shape retrieval system, and retrieval effectiveness assessed on a database of over 10,000 images, using 24 queries and associated ground truth supplied by the UK Patent Office . Our experiments revealed only minor differences in retrieval effectiveness between different measures, suggesting that a wide variety of shape feature combinations can provide adequate discriminating power for effective shape retrieval in multi-component image collections such as trademark registries. Marked differences between measures were observed for some individual queries, suggesting that there could be considerable scope for improving retrieval effectiveness by providing users with an improved framework for searching multi-dimensional feature space.

Item Type:	Conference or Workshop Item (Paper)
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	The International Society for Optical Engineering
ISSN:	0277786X
Related URLs:	Publisher
Last Modified:	18 Oct 2022 13:38
URI:	https://orca.cardiff.ac.uk/id/eprint/14693

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