| Texture image classification using multi-fractal dimension |
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| 引用本文： | LIU Zhuo-fu and SANG En-fang School of Underwater Acoustic Engineering,Harbin Engineering University,Harbin 150001,China. Texture image classification using multi-fractal dimension[J]. 船舶与海洋工程学报, 2003, 2(2): 76-81. DOI: 10.1007/BF02918668 |
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| 作者姓名： | LIU Zhuo-fu and SANG En-fang School of Underwater Acoustic Engineering Harbin Engineering University Harbin 150001 China |
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| 作者单位： | LIU Zhuo-fu and SANG En-fang School of Underwater Acoustic Engineering，Harbin Engineering University，Harbin 150001，China |
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| 摘 要： | This paper presents a supervised classification method of sonar image, which takes advantages of both muhi-fractal theory and wavelet analysis. In the process of feature extraction, image transformation and wavelet decomposition are combined and a feature set based on multi-fractal dimension is obtained. In the part of classifier construction, the Learning Vector Quantization (LVQ) network is adopted as a classifier. Experiments of sonar image classification were carried out with satisfactory resuits, which verify the effectiveness of this method.
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| 关 键 词： | 小波分析 声纳图像分类 构造分析 图像识别 分形维数 |
| 收稿时间： | 2008-03-30 |
Texture image classification using multi-fractal dimension |
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| Liu Zhuo-fu,Sang En-fang. Texture image classification using multi-fractal dimension[J]. Journal of Marine Science and Application, 2003, 2(2): 76-81. DOI: 10.1007/BF02918668 |
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| Authors: | Liu Zhuo-fu Sang En-fang |
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| Affiliation: | (1) School of Underwater Acoustic Engineering, Harbin Engineering University, 150001 Harbin, China |
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| Abstract: | This paper presents a supervised classification method of sonar image, which takes advantages of both multi-fractal theory and wavelet analysis. In the process of feature extraction, image transformation and wavelet decomposition are combined and a feature set based on multi-fractal dimension is obtained. In the part of classifier construction, the Learning Vector Quantization (LVQ) network is adopted as a classifier. Experiments of sonar image classification were carried out with satisfactory results, which verify the effectiveness of this method. |
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| Keywords: | wavelet analysis multi-fractal dimension sonar image classification texture LVQ classifier |
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