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Proceedings Paper

Image retrieval based on local grey-level invariants
Author(s): Eva Bordeaux; Neelima Shrikhande
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Paper Abstract

During past decades, the enormous growth of image archives has significantly increased the demand for research efforts aimed at efficiently finding specific images within large databases. This paper investigates matching of images of buildings, architectural designs, blueprints and sketches. Their geometrical constrains lead to the proposed approach: the use of local grey-level invariants based on internal contours of the object. The problem involves three key phases: object recognition in image data, matching two images and searching the database of images. The emphasis of this paper is on object recognition based on internal contours of image data. In her master's thesis, M.M. Kulkarni described a technique for image retrieval by contour analysis implemented on external contours of an object in an image data. This is used to define the category of a building (tower, dome, flat, etc). Integration of these results with local grey-level invariant analysis creates a more robust image retrieval system. Thus, the best match result is the intersection of the results of contour analysis and grey-level invariants analysis. Experiments conducted for the database of architectural buildings have shown robustness w.r.t. to image rotation, translation, small view-point variations, partial visibility and extraneous features. The recognition rate is above 99% for a variety of tested images taken under different conditions.

Paper Details

Date Published: 25 October 2005
PDF: 12 pages
Proc. SPIE 6006, Intelligent Robots and Computer Vision XXIII: Algorithms, Techniques, and Active Vision, 600607 (25 October 2005); doi: 10.1117/12.629682
Show Author Affiliations
Eva Bordeaux, Central Michigan Univ. (United States)
Neelima Shrikhande, Central Michigan Univ. (United States)


Published in SPIE Proceedings Vol. 6006:
Intelligent Robots and Computer Vision XXIII: Algorithms, Techniques, and Active Vision
David P. Casasent; Ernest L. Hall; Juha Röning, Editor(s)

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