Weibao, Zou and Li, Yan and Lo, King Chuen and Chi, Zheru (2006) Improvement of image classification with wavelet and independent component analysis (ICA) based on a structured neural network. In: 2006 International Joint Conference on Neural Networks (IJCNN), 16-21July 2006, Vancouver, Canada.
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Image classification is a challenging problem in organizing a large image database. However, an effective method for such an objective is still under investigation. This paper presents a method based on wavelet and Independent Analysis Component (ICA) for image classification with adaptive processing of data structures. With wavelet, an image is decomposed into low frequency bands and high frequency bands. An image can be characterized by wavelet coefficients in the form of tree representation. While the histograms of low frequency wavelet components are effective in characterizing images, the histograms of high frequency wavelet components are similar for different images and therefore they cannot be directly used as features. We make use of ICA for feature extraction from high frequency bands to improve image classification. Two sets of features are used together to classify images using a structured neural network. In total, 2940 images generated from seven categories are used in experiments. Half of the images are used for training neural network and the other images used for testing. The classification rate of the training set is 92%, and the classification rate of the test set reaches 89%. The experimental results show effectiveness of the proposed method based on combined wavelet and ICA for image classification.
|Item Type:||Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)|
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|Uncontrolled Keywords:||neural networks, image classification|
|Fields of Research (FOR2008):||08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080106 Image Processing|
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080108 Neural, Evolutionary and Fuzzy Computation
|Subjects:||280000 Information, Computing and Communication Sciences > 280200 Artificial Intelligence and Signal and Image Processing > 280212 Neural Networks, Genetic Alogrithms and Fuzzy Logic|
280000 Information, Computing and Communication Sciences > 280200 Artificial Intelligence and Signal and Image Processing > 280203 Image Processing
|Socio-Economic Objective (SEO2008):||E Expanding Knowledge > 97 Expanding Knowledge > 970101 Expanding Knowledge in the Mathematical Sciences|
|Deposited On:||11 Oct 2007 10:47|
|Last Modified:||11 Oct 2011 22:14|
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