The Experts below are selected from a list of 44064 Experts worldwide ranked by ideXlab platform
Fu Wang - One of the best experts on this subject based on the ideXlab platform.
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speech emotion recognition based on dnn decision tree SVM Model
Speech Communication, 2019Co-Authors: Linhui Sun, Bo Zou, Jia Chen, Fu WangAbstract:Abstract Motivated by the development of DNN technology, a speech emotion recognition method based on DNN-decision tree SVM Model is proposed. The proposed method can not only excavate the deep emotion information of the speech signal, but also extract more distinctive emotion features from the easily confused emotions. In this method, the decision tree SVM structure is firstly constructed by computing the confusion degree of emotion, and then different DNN are trained for diverse emotion groups to extract the bottleneck features that are used to train each SVM in the decision tree. Finally, speech emotion classification is realized based on this Model. This Model is assessed by using the Chinese Academy of Sciences Emotional Corpus. The experiment results show that the average emotion recognition rate based on the proposed method is 6.25% and 2.91% higher than traditional SVM and DNN-SVM classification method, respectively. It is proved that this method can effectively reduce the confusion between emotions, thus improving the speech emotion recognition rate.
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decision tree SVM Model with fisher feature selection for speech emotion recognition
Eurasip Journal on Audio Speech and Music Processing, 2019Co-Authors: Linhui Sun, Fu WangAbstract:The overall recognition rate will reduce due to the increase of emotional confusion in multiple speech emotion recognition. To solve the problem, we propose a speech emotion recognition method based on the decision tree support vector machine (SVM) Model with Fisher feature selection. At the stage of feature selection, Fisher criterion is used to filter out the feature parameters of higher distinguish ability. At the emotion classification stage, an algorithm is proposed to determine the structure of decision tree. The decision tree SVM can realize the two-step classification of the first rough classification and the fine classification. Thus the redundant parameters are eliminated and the performance of emotion recognition is improved. In this method, the decision tree SVM framework is firstly established by calculating the confusion degree of emotion, and then the features with higher distinguish ability are selected for each SVM of the decision tree according to Fisher criterion. Finally, speech emotion recognition is realized based on this Model. The decision tree SVM with Fisher feature selection on CASIA Chinese emotion speech corpus and Berlin speech corpus are constructed to validate the effectiveness of our framework. The experimental results show that the average emotion recognition rate based on the proposed method is 9% higher than traditional SVM classification method on CASIA, and 8.26% higher on Berlin speech corpus. It is verified that the proposed method can effectively reduce the emotional confusion and improve the emotion recognition rate.
Xiang-yang Wang - One of the best experts on this subject based on the ideXlab platform.
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ls SVM based image segmentation using color and texture information
Journal of Visual Communication and Image Representation, 2012Co-Authors: Hong-ying Yang, Xiang-yang Wang, Qinyan Wang, Xianjin ZhangAbstract:Image segmentation partitions an image into nonoverlapping regions, which ideally should be meaningful for a certain purpose. Automatic segmentation of images is a very challenging fundamental task in computer vision and one of the most crucial steps toward image understanding. In recent years, many image segmentation algorithms have been developed, but they are often very complex and some undesired results occur frequently. In this paper, we present an effective color image segmentation approach based on pixel classification with least squares support vector machine (LS-SVM). Firstly, the pixel-level color feature, Homogeneity, is extracted in consideration of local human visual sensitivity for color pattern variation in HSV color space. Secondly, the image pixel's texture features, Maximum local energy, Maximum gradient, and Maximum second moment matrix, are represented via Gabor filter. Then, both the pixel-level color feature and texture feature are used as input of LS-SVM Model (classifier), and the LS-SVM Model (classifier) is trained by selecting the training samples with Arimoto entropy thresholding. Finally, the color image is segmented with the trained LS-SVM Model (classifier). This image segmentation not only can fully take advantage of the local information of color image, but also the ability of LS-SVM classifier. Experimental evidence shows that the proposed method has very effective segmentation results and computational behavior, and decreases the time and increases the quality of color image segmentation in comparison with the state-of-the-art segmentation methods recently proposed in the literature.
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color image segmentation using automatic pixel classification with support vector machine
Neurocomputing, 2011Co-Authors: Xiang-yang Wang, Qinyan Wang, Hong-ying YangAbstract:Automatic segmentation of images is a very challenging fundamental task in computer vision and one of the most crucial steps toward image understanding. In this paper, we present a color image segmentation using automatic pixel classification with support vector machine (SVM). First, the pixel-level color feature is extracted in consideration of human visual sensitivity for color pattern variations, and the image pixel's texture feature is represented via steerable filter. Both the pixel-level color feature and texture feature are used as input of SVM Model (classifier). Then, the SVM Model (classifier) is trained by using fuzzy c-means clustering (FCM) with the extracted pixel-level features. Finally, the color image is segmented with the trained SVM Model (classifier). This image segmentation not only can fully take advantage of the local information of color image, but also the ability of SVM classifier. Experimental evidence shows that the proposed method has a very effective segmentation results and computational behavior, and decreases the time and increases the quality of color image segmentation in compare with the state-of-the-art segmentation methods recently proposed in the literature.
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color image segmentation using pixel wise support vector machine classification
Pattern Recognition, 2011Co-Authors: Xiang-yang Wang, Ting WangAbstract:Image segmentation is an important tool in image processing and can serve as an efficient front end to sophisticated algorithms and thereby simplify subsequent processing. In this paper, we present a color image segmentation using pixel wise support vector machine (SVM) classification. Firstly, the pixel-level color feature and texture feature of the image, which is used as input of SVM Model (classifier), are extracted via the local homogeneity Model and Gabor filter. Then, the SVM Model (classifier) is trained by using FCM with the extracted pixel-level features. Finally, the color image is segmented with the trained SVM Model (classifier). This image segmentation not only can fully take advantage of the local information of color image, but also the ability of SVM classifier. Experimental evidence shows that the proposed method has a very effective segmentation results and computational behavior, and decreases the time and increases the quality of color image segmentation in comparison with the state-of-the-art segmentation methods recently proposed in the literature.
Linhui Sun - One of the best experts on this subject based on the ideXlab platform.
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speech emotion recognition based on dnn decision tree SVM Model
Speech Communication, 2019Co-Authors: Linhui Sun, Bo Zou, Jia Chen, Fu WangAbstract:Abstract Motivated by the development of DNN technology, a speech emotion recognition method based on DNN-decision tree SVM Model is proposed. The proposed method can not only excavate the deep emotion information of the speech signal, but also extract more distinctive emotion features from the easily confused emotions. In this method, the decision tree SVM structure is firstly constructed by computing the confusion degree of emotion, and then different DNN are trained for diverse emotion groups to extract the bottleneck features that are used to train each SVM in the decision tree. Finally, speech emotion classification is realized based on this Model. This Model is assessed by using the Chinese Academy of Sciences Emotional Corpus. The experiment results show that the average emotion recognition rate based on the proposed method is 6.25% and 2.91% higher than traditional SVM and DNN-SVM classification method, respectively. It is proved that this method can effectively reduce the confusion between emotions, thus improving the speech emotion recognition rate.
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decision tree SVM Model with fisher feature selection for speech emotion recognition
Eurasip Journal on Audio Speech and Music Processing, 2019Co-Authors: Linhui Sun, Fu WangAbstract:The overall recognition rate will reduce due to the increase of emotional confusion in multiple speech emotion recognition. To solve the problem, we propose a speech emotion recognition method based on the decision tree support vector machine (SVM) Model with Fisher feature selection. At the stage of feature selection, Fisher criterion is used to filter out the feature parameters of higher distinguish ability. At the emotion classification stage, an algorithm is proposed to determine the structure of decision tree. The decision tree SVM can realize the two-step classification of the first rough classification and the fine classification. Thus the redundant parameters are eliminated and the performance of emotion recognition is improved. In this method, the decision tree SVM framework is firstly established by calculating the confusion degree of emotion, and then the features with higher distinguish ability are selected for each SVM of the decision tree according to Fisher criterion. Finally, speech emotion recognition is realized based on this Model. The decision tree SVM with Fisher feature selection on CASIA Chinese emotion speech corpus and Berlin speech corpus are constructed to validate the effectiveness of our framework. The experimental results show that the average emotion recognition rate based on the proposed method is 9% higher than traditional SVM classification method on CASIA, and 8.26% higher on Berlin speech corpus. It is verified that the proposed method can effectively reduce the emotional confusion and improve the emotion recognition rate.
Hong-ying Yang - One of the best experts on this subject based on the ideXlab platform.
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ls SVM based image segmentation using color and texture information
Journal of Visual Communication and Image Representation, 2012Co-Authors: Hong-ying Yang, Xiang-yang Wang, Qinyan Wang, Xianjin ZhangAbstract:Image segmentation partitions an image into nonoverlapping regions, which ideally should be meaningful for a certain purpose. Automatic segmentation of images is a very challenging fundamental task in computer vision and one of the most crucial steps toward image understanding. In recent years, many image segmentation algorithms have been developed, but they are often very complex and some undesired results occur frequently. In this paper, we present an effective color image segmentation approach based on pixel classification with least squares support vector machine (LS-SVM). Firstly, the pixel-level color feature, Homogeneity, is extracted in consideration of local human visual sensitivity for color pattern variation in HSV color space. Secondly, the image pixel's texture features, Maximum local energy, Maximum gradient, and Maximum second moment matrix, are represented via Gabor filter. Then, both the pixel-level color feature and texture feature are used as input of LS-SVM Model (classifier), and the LS-SVM Model (classifier) is trained by selecting the training samples with Arimoto entropy thresholding. Finally, the color image is segmented with the trained LS-SVM Model (classifier). This image segmentation not only can fully take advantage of the local information of color image, but also the ability of LS-SVM classifier. Experimental evidence shows that the proposed method has very effective segmentation results and computational behavior, and decreases the time and increases the quality of color image segmentation in comparison with the state-of-the-art segmentation methods recently proposed in the literature.
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color image segmentation using automatic pixel classification with support vector machine
Neurocomputing, 2011Co-Authors: Xiang-yang Wang, Qinyan Wang, Hong-ying YangAbstract:Automatic segmentation of images is a very challenging fundamental task in computer vision and one of the most crucial steps toward image understanding. In this paper, we present a color image segmentation using automatic pixel classification with support vector machine (SVM). First, the pixel-level color feature is extracted in consideration of human visual sensitivity for color pattern variations, and the image pixel's texture feature is represented via steerable filter. Both the pixel-level color feature and texture feature are used as input of SVM Model (classifier). Then, the SVM Model (classifier) is trained by using fuzzy c-means clustering (FCM) with the extracted pixel-level features. Finally, the color image is segmented with the trained SVM Model (classifier). This image segmentation not only can fully take advantage of the local information of color image, but also the ability of SVM classifier. Experimental evidence shows that the proposed method has a very effective segmentation results and computational behavior, and decreases the time and increases the quality of color image segmentation in compare with the state-of-the-art segmentation methods recently proposed in the literature.
Chuangbing Zhou - One of the best experts on this subject based on the ideXlab platform.
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landslide displacement prediction based on multivariate chaotic Model and extreme learning machine
Engineering Geology, 2017Co-Authors: Faming Huang, Jinsong Huang, Shuihua Jiang, Chuangbing ZhouAbstract:Abstract This paper proposes a multivariate chaotic Extreme Learning Machine (ELM) Model for the prediction of the displacement of reservoir landslides. The displacement time series of the Baishuihe and Bazimen landslides in the Three Gorges Reservoir Area in China are used as examples. The results show that there are evidences of chaos in the displacement time series. The univariate chaotic ELM Model and the multivariate chaotic Model based on Particle Swarm Optimization and Support Vector Machine (PSO-SVM) Model are also applied for the purpose of comparison. The comparisons show that the multivariate chaotic ELM Model achieves higher prediction accuracy than the univariate chaotic ELM Model and the multivariate chaotic PSO-SVM Model.
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landslide displacement prediction based on multivariate chaotic Model and extreme learning machine
Engineering Geology, 2017Co-Authors: Faming Huang, Jinsong Huang, Shuihua Jiang, Chuangbing ZhouAbstract:Abstract This paper proposes a multivariate chaotic Extreme Learning Machine (ELM) Model for the prediction of the displacement of reservoir landslides. The displacement time series of the Baishuihe and Bazimen landslides in the Three Gorges Reservoir Area in China are used as examples. The results show that there are evidences of chaos in the displacement time series. The univariate chaotic ELM Model and the multivariate chaotic Model based on Particle Swarm Optimization and Support Vector Machine (PSO-SVM) Model are also applied for the purpose of comparison. The comparisons show that the multivariate chaotic ELM Model achieves higher prediction accuracy than the univariate chaotic ELM Model and the multivariate chaotic PSO-SVM Model.
