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Realizing the automated and online detection of crowd anomalies from surveillance CCTVs is a research-intensive and application-demanding task. This research proposes a novel technique for detecting crowd abnormalities through analyzing the spatial and temporal features of the input video signals. This integrated solution defines an image descriptor that reflects the global motion information over...
Two GF-1 WFV images on August 3, 2015 and October 2, 2015 were selected to extract the cultivated area of paddy rice in Jianhu county of Jiangsu Province. Vegetation indexes were extracted from the original spectrum data in order to extract paddy rice area with Maximum Likelihood Classifier (MLC), Support Vector Machine (SVM) and Classification and Regression Trees (CART). The extraction accuracy...
Feature extraction is of great importance in condition monitoring and fault diagnosis of rolling machinery. Nonlinear dimensionality reduction (NDR) theories brought a new idea for recognizing and predicting the underlying nonlinear behavior. In this paper, we propose a NDR based feature extraction method for fault classification of rolling element bearing. Original feature spaces are constructed...
Text classification has been considered as a hot research area in data mining. This paper presents a new approach combining hidden Markov model (HMM) with support vector machine (SVM) for text classification. HMMs are used to as a feature extractor and then a new feature vector is normalized as the input of SVMs, so the trained SVMs can classify unknown texts successfully. The experimental results...
Decision tree is one common method used in data mining to extract predicted information. Based on Statistical Learning Theory (SLT), support vector machine(SVM) is a new kind of machine learning method that is used for classification and regression, it realizes the trade-off between empirical risk minimization(ERM) and generalization capability. SVM and decision tree have combined into one multi-class...
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