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Proceedings Paper

Content-based image classification using quasi-Gabor filters
Author(s): Liya Chen; Jianhua Li
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Paper Abstract

This paper proposed an approach of online content filtering system, which can filter unexpected content from Internet, support searching, detecting, recognizing images, video and multimedia data. The approach consists of three parts: first is texture feature extraction with quasi-Gabor filters. These filters are constructed in different directions and sizes in frequency domain of images. This avoids convolution and multiplication with images spatially. Second, the extracted features are sent to Kohonon neural networks to perform decreasing dimension. The outputs of Kohonon network are then fed to a neural network classifier to get the final classification result. The proposed approach has been applied in our content monitoring system, which can filter unexpected images and alarm by pre-defined requirement.

Paper Details

Date Published: 18 May 2004
PDF: 7 pages
Proc. SPIE 5297, Real-Time Imaging VIII, (18 May 2004); doi: 10.1117/12.527243
Show Author Affiliations
Liya Chen, Shanghai Jiaotong Univ. (China)
Jianhua Li, Shanghai Jiaotong Univ. (China)

Published in SPIE Proceedings Vol. 5297:
Real-Time Imaging VIII
Nasser Kehtarnavaz; Phillip A. Laplante, Editor(s)

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