title: Particulate matter characterization by gray level co-occurrence matrix based support vector machines.
abstract: 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.
authors.affiliation: EECS Department, University of Toledo, Toledo, OH 43606, USA. ktiruma@rockets.utoledo.edu
authors.forename: K
authors.initials: K
authors.lastname: Manivannan
authors.forename: P
authors.initials: P
authors.lastname: Aggarwal
authors.forename: V
authors.initials: V
authors.lastname: Devabhaktuni
authors.forename: A
authors.initials: A
authors.lastname: Kumar
authors.forename: D
authors.initials: D
authors.lastname: Nims
authors.forename: P
authors.initials: P
authors.lastname: Bhattacharya
mesh_terms: D000393:Air Pollutants / Q000032:analysis*; D000465:Algorithms; D001185:Artificial Intelligence; D004784:Environmental Monitoring / Q000295:instrumentation / Q000379:methods; D008855:Microscopy, Electron, Scanning; D016571:Neural Networks, Computer; D010316:Particle Size; D052638:Particulate Matter / Q000032:analysis*; D012815:Signal Processing, Computer-Assisted
chemical_list: D000393:Air Pollutants; D052638:Particulate Matter