18 May 2004 Content-based image classification using quasi-Gabor filters
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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.
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Liya Chen, Liya Chen, Jianhua Li, Jianhua Li, } "Content-based image classification using quasi-Gabor filters", Proc. SPIE 5297, Real-Time Imaging VIII, (18 May 2004); doi: 10.1117/12.527243; https://doi.org/10.1117/12.527243

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