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Proceedings Paper

Adaptive shape transform for color image querying
Author(s): Mehmet Celenk; Qiang Zhou; Vermund Vetnes; Rakesh K. Godavari
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Paper Abstract

Spectral (color) and spatial (shape) features available in pictures are sources of information that need to be incorporated for advance content-based image database retrieval. The adaptive shape transform approach developed in this research is originated from the premise that a two-dimensional (2D) shape can be recovered completely from a set of the orthogonal Radon transform-based projections. For search consistency, it is necessary to identify the region(s) of interest (ROI) before applying the Radon transform to shape query. ROI’s are detected automatically by means of saliency map-based segmentation. The Radon transform packs the shape information of a 2D mess along the projection axis of known orientation, and generates a series of one-dimensional (1D) functions from color channels for projection angles ranging from 1° to 180°. The optimal number of projections for a particular shape is determined by imposing the Kullback-Leibler distance (KLD) histogram comparison as the similarity metric between the query and database images. The Radon transforms with the shortest and longest lengths yield the most distinctive shape attributes for the object classes being queried. For translation- and rotation-invariant retrieval, the principal component analysis is utilized as the preprocessing tool in the spatial plane. Size invariance is achieved by normalizing the Radon transforms in the (R, G, B) color channels independently. The proposed algorithm was tested on a wide range of complex shaped objects imaged in 24-bit color with different spatial resolutions. The KLDs between two images are calculated in the longest and shortest directions of the Radon transform, and then are added together to find the similarity measure corresponding to the query and database pictures. Higher measures indicate two dissimilar shapes, while smaller values represent two similar ones. Experimental results show that the method is robust and accounts for high noise immunity.

Paper Details

Date Published: 28 May 2003
PDF: 13 pages
Proc. SPIE 5014, Image Processing: Algorithms and Systems II, (28 May 2003); doi: 10.1117/12.477747
Show Author Affiliations
Mehmet Celenk, Ohio Univ. (United States)
Qiang Zhou, Ohio Univ. (United States)
Vermund Vetnes, Ohio Univ. (United States)
Rakesh K. Godavari, Ohio Univ. (United States)

Published in SPIE Proceedings Vol. 5014:
Image Processing: Algorithms and Systems II
Edward R. Dougherty; Jaakko T. Astola; Karen O. Egiazarian, Editor(s)

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