Particulate matter characterization by gray level co-occurrence matrix based support vector machines

EECS Department, University of Toledo, Toledo, OH 43606, USA.
Journal of hazardous materials (Impact Factor: 4.53). 04/2012; 223-224:94-103. DOI: 10.1016/j.jhazmat.2012.04.056
Source: PubMed


An efficient and highly reliable automatic selection of optimal segmentation algorithm for characterizing particulate matter is presented in this paper. Support vector machines (SVMs) are used as a new self-regulating classifier trained by gray level co-occurrence matrix (GLCM) of the image. This matrix is calculated at various angles and the texture features are evaluated for classifying the images. Results show that the performance of GLCM-based SVMs is drastically improved over the previous histogram-based SVMs. Our proposed GLCM-based approach of training SVM predicts a robust and more accurate segmentation algorithm than the standard histogram technique, as additional information based on the spatial relationship between pixels is incorporated for image classification. Further, the GLCM-based SVM classifiers were more accurate and required less training data when compared to the artificial neural network (ANN) classifiers.

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