27 October 2013 Better image texture recognition based on SVM classification
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Proceedings Volume 8919, MIPPR 2013: Pattern Recognition and Computer Vision; 89190W (2013) https://doi.org/10.1117/12.2031539
Event: Eighth International Symposium on Multispectral Image Processing and Pattern Recognition, 2013, Wuhan, China
Abstract
Texture classification is very important in remote sensing images, X-ray photos, cell image interpretation and processing, and is also the active research areas of computer vision, image processing, image analysis, image retrieval, and so on. As to spatial domain image, texture analysis can use statistical methods to calculate the texture feature vector. In this paper, we use the gray level co-occurrence matrix and Gabor filter feature vector to calculate the feature vector. For the feature vector classification under normal circumstances we can use Bayesian method, KNN method, BP neural network. In this paper, we use a statistical classification method which is based on SVM method to classify images. Image classification generally includes image preprocessing, image feature extraction, image feature selection and image classification in four steps. In this paper, we use a gray-scale image, by calculating the image gray level cooccurrence matrix and Gabor filtering method to get feature extraction, and then use SVM to training and classification. From the test results, it shows that the SVM method is the better way to solve the problem of texture features for image classification and it shows strong adaptability and robustness for image classification.
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Kuan Liu, Bin Lu, Yaxun Wei, "Better image texture recognition based on SVM classification", Proc. SPIE 8919, MIPPR 2013: Pattern Recognition and Computer Vision, 89190W (27 October 2013); doi: 10.1117/12.2031539; https://doi.org/10.1117/12.2031539
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