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Proceedings Paper

Radon transform, bispectra, and principal component analysis for RTS invariant image retrieval
Author(s): Yuan Shao; Mehmet Celenk
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Paper Abstract

An image retrieval method is presented based on shape similarity measure for multimedia and imaging database system. In the proposed algorithm, the spatial and spectral properties of images are combined using the Radon transform, bispectra, and principal components analysis. For each model image in the database, the original 2D image data are reduced to a set of 1D projections via the Radon transform, and then a feature vector is calculated from the bispectra of the resultant 1D functions. The principal component analysis is applied to further reduce the dimension of the feature vector so that it can be stored along with the original image in the database at a small cost of memory. The derived feature vector is considered as the index or key of the corresponding image, which uniquely identifies the image independent of rotation, translation, and scaling. For image retrieval, the data feature vector is computed for a query image, and matched against the feature vectors of all the model images in the database using the Tanimoto similarity measure. The closely matching images are brought out as the searching results. The proposed technique has been tested on a large image database. The experimental results show that the retrieval accuracy is very high even for query images with low signal-to-noise ratio.

Paper Details

Date Published: 24 August 1999
PDF: 8 pages
Proc. SPIE 3846, Multimedia Storage and Archiving Systems IV, (24 August 1999); doi: 10.1117/12.360427
Show Author Affiliations
Yuan Shao, Ohio Univ. (United States)
Mehmet Celenk, Ohio Univ. (United States)


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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