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Proceedings Paper

Application of contour feature classes to object-based image retrieval
Author(s): Kanbin Ge; Shunichiro Oe
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Paper Abstract

Currently, image retrieval system are based on low level features of color, texture and shape, not on the semantic descriptions that are common to humans, such as objects, people, and place. In order to narrow down the gap between the low level and semantic level, in this study, we describe an efficient and effective image similarity calculation method for image comparison at object classes. It is not only suitable for images with single objects, but also for images containing multiple and partially occluded objects. In this approach, a machine learning algorithm is used to predict the classes of each of object-contour segments. The similarity measure between two images is been computed using Euclidean distance between images in the k-dimensional space. Experimental results show that this approach is effective, and is invariant to rotation, scaling, and translation of objects.

Paper Details

Date Published: 30 August 2002
PDF: 8 pages
Proc. SPIE 4925, Electronic Imaging and Multimedia Technology III, (30 August 2002); doi: 10.1117/12.481560
Show Author Affiliations
Kanbin Ge, Univ. of Tokushima (Japan)
Shunichiro Oe, Univ. of Tokushima (Japan)

Published in SPIE Proceedings Vol. 4925:
Electronic Imaging and Multimedia Technology III
LiWei Zhou; Chung-Sheng Li; Yoshiji Suzuki, Editor(s)

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