Urban tree species provide various essential ecosystem services in cities,such as regulating urban temperatures,reducing noise,capturing carbon,and mitigating the urban heat island effect.The quality of these services...Urban tree species provide various essential ecosystem services in cities,such as regulating urban temperatures,reducing noise,capturing carbon,and mitigating the urban heat island effect.The quality of these services is influenced by species diversity,tree health,and the distribution and the composition of trees.Traditionally,data on urban trees has been collected through field surveys and manual interpretation of remote sensing images.In this study,we evaluated the effectiveness of multispectral airborne laser scanning(ALS)data in classifying 24 common urban roadside tree species in Espoo,Finland.Tree crown structure information,intensity features,and spectral data were used for classification.Eight different machine learning algorithms were tested,with the extra trees(ET)algorithm performing the best,achieving an overall accuracy of 71.7%using multispectral LiDAR data.This result highlights that integrating structural and spectral information within a single framework can improve the classification accuracy.Future research will focus on identifying the most important features for species classification and developing algorithms with greater efficiency and accuracy.展开更多
In response to the scarcity of infrared aircraft samples and the tendency of traditional deep learning to overfit,a few-shot infrared aircraft classification method based on cross-correlation networks is proposed.This...In response to the scarcity of infrared aircraft samples and the tendency of traditional deep learning to overfit,a few-shot infrared aircraft classification method based on cross-correlation networks is proposed.This method combines two core modules:a simple parameter-free self-attention and cross-attention.By analyzing the self-correlation and cross-correlation between support images and query images,it achieves effective classification of infrared aircraft under few-shot conditions.The proposed cross-correlation network integrates these two modules and is trained in an end-to-end manner.The simple parameter-free self-attention is responsible for extracting the internal structure of the image while the cross-attention can calculate the cross-correlation between images further extracting and fusing the features between images.Compared with existing few-shot infrared target classification models,this model focuses on the geometric structure and thermal texture information of infrared images by modeling the semantic relevance between the features of the support set and query set,thus better attending to the target objects.Experimental results show that this method outperforms existing infrared aircraft classification methods in various classification tasks,with the highest classification accuracy improvement exceeding 3%.In addition,ablation experiments and comparative experiments also prove the effectiveness of the method.展开更多
近日,郑州大学网络空间安全学院在医学图像处理方向取得进展,相关研究成果以题为“PointFormer:Keypoint-Guided Transformer for Simultaneous Nuclei Segmentation and Classification in Multi-Tissue Histology Images”的论文在线...近日,郑州大学网络空间安全学院在医学图像处理方向取得进展,相关研究成果以题为“PointFormer:Keypoint-Guided Transformer for Simultaneous Nuclei Segmentation and Classification in Multi-Tissue Histology Images”的论文在线发表在国际权威期刊《IEEE Transactions on Image Processing》(中科院一区TOP,CCF-A类期刊,IF=10.8)和以题为“SimCMC:A Simple Compact Multiview Contrastive Framework for Self-supervised Early Alzheimer’s Disease Diagnosis”的论文在线发表在国际权威期刊《IEEE Transactions on Instrumentation and Measurement》(中科院二区TOP,IF=5.6)。展开更多
In this paper,we propose hierarchical attention dual network(DNet)for fine-grained image classification.The DNet can randomly select pairs of inputs from the dataset and compare the differences between them through hi...In this paper,we propose hierarchical attention dual network(DNet)for fine-grained image classification.The DNet can randomly select pairs of inputs from the dataset and compare the differences between them through hierarchical attention feature learning,which are used simultaneously to remove noise and retain salient features.In the loss function,it considers the losses of difference in paired images according to the intra-variance and inter-variance.In addition,we also collect the disaster scene dataset from remote sensing images and apply the proposed method to disaster scene classification,which contains complex scenes and multiple types of disasters.Compared to other methods,experimental results show that the DNet with hierarchical attention is robust to different datasets and performs better.展开更多
With the popularization of social media,public opi-nion information on emergencies spreads rapidly on the Internet,the impact of negative public opinions on an event has become more significant.Based on the organizati...With the popularization of social media,public opi-nion information on emergencies spreads rapidly on the Internet,the impact of negative public opinions on an event has become more significant.Based on the organizational form of public opinion information,the knowledge graph is used to construct the knowledge base of public opinion risk cases on the emer-gency network.The emotion recognition model of negative pub-lic opinion information based on the bi-directional long short-term memory(BiLSTM)network is studied in the model layer design,and a linear discriminant analysis(LDA)topic extraction method combined with association rules is proposed to extract and mine the semantics of negative public opinion topics to real-ize further in-depth analysis of information topics.Focusing on public health emergencies,knowledge acquisition and knowl-edge processing of public opinion information are conducted,and the experimental results show that the knowledge graph framework based on the construction can facilitate in-depth theme evolution analysis of public opinion events,thus demon-strating important research significance for reducing online pub-lic opinion risks.展开更多
In order to obtain better inverse synthetic aperture radar(ISAR)image,a novel structure-enhanced spatial spectrum is proposed for estimating the incoherence parameters and fusing multiband.The proposed method takes fu...In order to obtain better inverse synthetic aperture radar(ISAR)image,a novel structure-enhanced spatial spectrum is proposed for estimating the incoherence parameters and fusing multiband.The proposed method takes full advantage of the original electromagnetic scattering data and its conjugated form by combining them with the novel covariance matrices.To analyse the superiority of the modified algorithm,the mathematical expression of equivalent signal to noise ratio(SNR)is derived,which can validate our proposed algorithm theoretically.In addition,compared with the conventional matrix pencil(MP)algorithm and the conventional root-multiple signal classification(Root-MUSIC)algorithm,the proposed algorithm has better parameter estimation performance and more accurate multiband fusion results at the same SNR situations.Validity and effectiveness of the proposed algorithm is demonstrated by simulation data and real radar data.展开更多
To better complete various missions, it is necessary to plan an optimal trajectory or provide the optimal control law for the multirole missile according to the actual situation, including launch conditions and target...To better complete various missions, it is necessary to plan an optimal trajectory or provide the optimal control law for the multirole missile according to the actual situation, including launch conditions and target location. Since trajectory optimization struggles to meet real-time requirements, the emergence of data-based generation methods has become a significant focus in contemporary research. However, due to the large differences in the characteristics of the optimal control laws caused by the diversity of tasks, it is difficult to achieve good prediction results by modeling all data with one single model.Therefore, the modeling idea of the mixture of experts(MoE) is adopted. Firstly, the K-means clustering algorithm is used to partition the sample data set, and the corresponding neural network classification model is established as the gate switch of MoE. Then, the expert models, i.e., the mappings from the generation conditions to the optimal control law represented by the results of principal component analysis(PCA), are represented by Kriging models. Finally, multiple rounds of accuracy evaluation, sample supplementation, and model updating are conducted to improve the generation accuracy. The Monte Carlo simulation shows that the accuracy of the proposed model reaches 96% and the generation efficiency meets the real-time requirement.展开更多
本文研究了一种新的高频地波超视距雷达目标距离以及方位角超分辨问题.在该雷达系统中,各个发射阵元采用不同的发射载频,因此目标回波信号中存在目标距离与方位角的耦合,本文提出利用这种耦合关系,采用M U S IC(M u ltip le S igna l C ...本文研究了一种新的高频地波超视距雷达目标距离以及方位角超分辨问题.在该雷达系统中,各个发射阵元采用不同的发射载频,因此目标回波信号中存在目标距离与方位角的耦合,本文提出利用这种耦合关系,采用M U S IC(M u ltip le S igna l C lassifica tion)算法获得目标距离以及方位角的超分辩,从而提高在多目标环境下测距、测角精度.仿真结果验证了该方法的有效性.展开更多
提出了基于2q阶累积量的非圆信号测向MUSIC(Multiple Signal Classification)算法(称为NC-2q-MUSIC),作为2q-MUSIC算法利用非圆信息的一种扩展,在可测向信号数、分辨力和测角精度等方面的性能均优于2q-MUSIC算法.并且,q越大,NC-2q-MUSI...提出了基于2q阶累积量的非圆信号测向MUSIC(Multiple Signal Classification)算法(称为NC-2q-MUSIC),作为2q-MUSIC算法利用非圆信息的一种扩展,在可测向信号数、分辨力和测角精度等方面的性能均优于2q-MUSIC算法.并且,q越大,NC-2q-MUSIC算法的可测向信号数越大,分辨力越高,对模型误差也越不敏感.针对均布线阵(ULA:Uniform Linear Array)提出的NC-2q-MUSIC/ULA算法减小了计算量.仿真实验验证了NC-2q-MUSIC算法的优良性能.展开更多
由于MUSIC(MUltiple SIgnal Classification)算法需要大量的乘法运算和三角函数求值,导致其实时处理能力较弱。为此,该文首先对均匀线阵和均匀圆阵的阵列结构进行分析,提取导向矢量的一些性质。然后,利用Hermite矩阵的性质对复数乘法进...由于MUSIC(MUltiple SIgnal Classification)算法需要大量的乘法运算和三角函数求值,导致其实时处理能力较弱。为此,该文首先对均匀线阵和均匀圆阵的阵列结构进行分析,提取导向矢量的一些性质。然后,利用Hermite矩阵的性质对复数乘法进行分解,再组建两个实值向量以减少乘法运算次数。最后,利用导向矢量的性质提出一种基于查表的新算法。新算法既没有三角函数求值运算,又不需要大量的存储空间。仿真实验结果表明新算法在没有改变MUSIC算法谱估计的效果的前提下,将MUSIC算法的运算速率提高了50倍以上。因此,新算法具有广阔的应用前景。展开更多
Coalbed methane enrichment will be controlled by many good macro geological dynamical conditions; there is evident difference of enrichment grade in different area and different geological conditions.This paper has st...Coalbed methane enrichment will be controlled by many good macro geological dynamical conditions; there is evident difference of enrichment grade in different area and different geological conditions.This paper has studied tectonic dynamical conditions, thermal dynamical conditions and hydraulic conditions, which affect coalbed methane enrichment in Qinshui basin.Coalbed methane enrichment units have been divided based on tectonic dynamical conditions of Qinshui basin,combined with thermal dynamical conditions and hydraulic conditions.展开更多
The sharp increase of the amount of Internet Chinese text data has significantly prolonged the processing time of classification on these data.In order to solve this problem,this paper proposes and implements a parall...The sharp increase of the amount of Internet Chinese text data has significantly prolonged the processing time of classification on these data.In order to solve this problem,this paper proposes and implements a parallel naive Bayes algorithm(PNBA)for Chinese text classification based on Spark,a parallel memory computing platform for big data.This algorithm has implemented parallel operation throughout the entire training and prediction process of naive Bayes classifier mainly by adopting the programming model of resilient distributed datasets(RDD).For comparison,a PNBA based on Hadoop is also implemented.The test results show that in the same computing environment and for the same text sets,the Spark PNBA is obviously superior to the Hadoop PNBA in terms of key indicators such as speedup ratio and scalability.Therefore,Spark-based parallel algorithms can better meet the requirement of large-scale Chinese text data mining.展开更多
Microseismic monitoring system is one of the effective methods for deep mining geo-stress monitoring.The principle of microseismic monitoring system is to analyze the mechanical parameters contained in microseismic ev...Microseismic monitoring system is one of the effective methods for deep mining geo-stress monitoring.The principle of microseismic monitoring system is to analyze the mechanical parameters contained in microseismic events for providing accurate information of rockmass.The accurate identification of microseismic events and blasts determines the timeliness and accuracy of early warning of microseismic monitoring technology.An image identification model based on Convolutional Neural Network(CNN)is established in this paper for the seismic waveforms of microseismic events and blasts.Firstly,the training set,test set,and validation set are collected,which are composed of 5250,1500,and 750 seismic waveforms of microseismic events and blasts,respectively.The classified data sets are preprocessed and input into the constructed CNN in CPU mode for training.Results show that the accuracies of microseismic events and blasts are 99.46%and 99.33%in the test set,respectively.The accuracies of microseismic events and blasts are 100%and 98.13%in the validation set,respectively.The proposed method gives superior performance when compared with existed methods.The accuracies of models using logistic regression and artificial neural network(ANN)based on the same data set are 54.43%and 67.9%in the test set,respectively.Then,the ROC curves of the three models are obtained and compared,which show that the CNN gives an absolute advantage in this classification model when the original seismic waveform are used in training the model.It not only decreases the influence of individual differences in experience,but also removes the errors induced by source and waveform parameters.It is proved that the established discriminant method improves the efficiency and accuracy of microseismic data processing for monitoring rock instability and seismicity.展开更多
To solve the multi-class fault diagnosis tasks, decision tree support vector machine （DTSVM）, which combines SVM and decision tree using the concept of dichotomy, is proposed. Since the classification performance of...To solve the multi-class fault diagnosis tasks, decision tree support vector machine （DTSVM）, which combines SVM and decision tree using the concept of dichotomy, is proposed. Since the classification performance of DTSVM highly depends on its structure, to cluster the multi-classes with maximum distance between the clustering centers of the two sub-classes, genetic algorithm is introduced into the formation of decision tree, so that the most separable classes would be separated at each node of decisions tree. Numerical simulations conducted on three datasets compared with ＂one-against-all＂ and ＂one-against-one＂ demonstrate the proposed method has better performance and higher generalization ability than the two conventional methods.展开更多
Facial expression recognition is a hot topic in computer vision, but it remains challenging due to the feature inconsistency caused by person-specific 'characteristics of facial expressions. To address such a chal...Facial expression recognition is a hot topic in computer vision, but it remains challenging due to the feature inconsistency caused by person-specific 'characteristics of facial expressions. To address such a challenge, and inspired by the recent success of deep identity network (DeepID-Net) for face identification, this paper proposes a novel deep learning based framework for recognising human expressions with facial images. Compared to the existing deep learning methods, our proposed framework, which is based on multi-scale global images and local facial patches, can significantly achieve a better performance on facial expression recognition. Finally, we verify the effectiveness of our proposed framework through experiments on the public benchmarking datasets JAFFE and extended Cohn-Kanade (CK+).展开更多
文摘Urban tree species provide various essential ecosystem services in cities,such as regulating urban temperatures,reducing noise,capturing carbon,and mitigating the urban heat island effect.The quality of these services is influenced by species diversity,tree health,and the distribution and the composition of trees.Traditionally,data on urban trees has been collected through field surveys and manual interpretation of remote sensing images.In this study,we evaluated the effectiveness of multispectral airborne laser scanning(ALS)data in classifying 24 common urban roadside tree species in Espoo,Finland.Tree crown structure information,intensity features,and spectral data were used for classification.Eight different machine learning algorithms were tested,with the extra trees(ET)algorithm performing the best,achieving an overall accuracy of 71.7%using multispectral LiDAR data.This result highlights that integrating structural and spectral information within a single framework can improve the classification accuracy.Future research will focus on identifying the most important features for species classification and developing algorithms with greater efficiency and accuracy.
基金Supported by the National Pre-research Program during the 14th Five-Year Plan(514010405)。
文摘In response to the scarcity of infrared aircraft samples and the tendency of traditional deep learning to overfit,a few-shot infrared aircraft classification method based on cross-correlation networks is proposed.This method combines two core modules:a simple parameter-free self-attention and cross-attention.By analyzing the self-correlation and cross-correlation between support images and query images,it achieves effective classification of infrared aircraft under few-shot conditions.The proposed cross-correlation network integrates these two modules and is trained in an end-to-end manner.The simple parameter-free self-attention is responsible for extracting the internal structure of the image while the cross-attention can calculate the cross-correlation between images further extracting and fusing the features between images.Compared with existing few-shot infrared target classification models,this model focuses on the geometric structure and thermal texture information of infrared images by modeling the semantic relevance between the features of the support set and query set,thus better attending to the target objects.Experimental results show that this method outperforms existing infrared aircraft classification methods in various classification tasks,with the highest classification accuracy improvement exceeding 3%.In addition,ablation experiments and comparative experiments also prove the effectiveness of the method.
文摘近日,郑州大学网络空间安全学院在医学图像处理方向取得进展,相关研究成果以题为“PointFormer:Keypoint-Guided Transformer for Simultaneous Nuclei Segmentation and Classification in Multi-Tissue Histology Images”的论文在线发表在国际权威期刊《IEEE Transactions on Image Processing》(中科院一区TOP,CCF-A类期刊,IF=10.8)和以题为“SimCMC:A Simple Compact Multiview Contrastive Framework for Self-supervised Early Alzheimer’s Disease Diagnosis”的论文在线发表在国际权威期刊《IEEE Transactions on Instrumentation and Measurement》(中科院二区TOP,IF=5.6)。
基金Supported by the National Natural Science Foundation of China(61601176)。
文摘In this paper,we propose hierarchical attention dual network(DNet)for fine-grained image classification.The DNet can randomly select pairs of inputs from the dataset and compare the differences between them through hierarchical attention feature learning,which are used simultaneously to remove noise and retain salient features.In the loss function,it considers the losses of difference in paired images according to the intra-variance and inter-variance.In addition,we also collect the disaster scene dataset from remote sensing images and apply the proposed method to disaster scene classification,which contains complex scenes and multiple types of disasters.Compared to other methods,experimental results show that the DNet with hierarchical attention is robust to different datasets and performs better.
基金supported by the National Social Science Foundation Major Project(22&ZD135)the National Social Science Fund National Emergency Management System Construction Research Project(20VYJ061).
文摘With the popularization of social media,public opi-nion information on emergencies spreads rapidly on the Internet,the impact of negative public opinions on an event has become more significant.Based on the organizational form of public opinion information,the knowledge graph is used to construct the knowledge base of public opinion risk cases on the emer-gency network.The emotion recognition model of negative pub-lic opinion information based on the bi-directional long short-term memory(BiLSTM)network is studied in the model layer design,and a linear discriminant analysis(LDA)topic extraction method combined with association rules is proposed to extract and mine the semantics of negative public opinion topics to real-ize further in-depth analysis of information topics.Focusing on public health emergencies,knowledge acquisition and knowl-edge processing of public opinion information are conducted,and the experimental results show that the knowledge graph framework based on the construction can facilitate in-depth theme evolution analysis of public opinion events,thus demon-strating important research significance for reducing online pub-lic opinion risks.
文摘In order to obtain better inverse synthetic aperture radar(ISAR)image,a novel structure-enhanced spatial spectrum is proposed for estimating the incoherence parameters and fusing multiband.The proposed method takes full advantage of the original electromagnetic scattering data and its conjugated form by combining them with the novel covariance matrices.To analyse the superiority of the modified algorithm,the mathematical expression of equivalent signal to noise ratio(SNR)is derived,which can validate our proposed algorithm theoretically.In addition,compared with the conventional matrix pencil(MP)algorithm and the conventional root-multiple signal classification(Root-MUSIC)algorithm,the proposed algorithm has better parameter estimation performance and more accurate multiband fusion results at the same SNR situations.Validity and effectiveness of the proposed algorithm is demonstrated by simulation data and real radar data.
基金Defense Industrial Technology Development Program (JCKY2020204B016)National Natural Science Foundation of China (92471206)。
文摘To better complete various missions, it is necessary to plan an optimal trajectory or provide the optimal control law for the multirole missile according to the actual situation, including launch conditions and target location. Since trajectory optimization struggles to meet real-time requirements, the emergence of data-based generation methods has become a significant focus in contemporary research. However, due to the large differences in the characteristics of the optimal control laws caused by the diversity of tasks, it is difficult to achieve good prediction results by modeling all data with one single model.Therefore, the modeling idea of the mixture of experts(MoE) is adopted. Firstly, the K-means clustering algorithm is used to partition the sample data set, and the corresponding neural network classification model is established as the gate switch of MoE. Then, the expert models, i.e., the mappings from the generation conditions to the optimal control law represented by the results of principal component analysis(PCA), are represented by Kriging models. Finally, multiple rounds of accuracy evaluation, sample supplementation, and model updating are conducted to improve the generation accuracy. The Monte Carlo simulation shows that the accuracy of the proposed model reaches 96% and the generation efficiency meets the real-time requirement.
文摘本文研究了一种新的高频地波超视距雷达目标距离以及方位角超分辨问题.在该雷达系统中,各个发射阵元采用不同的发射载频,因此目标回波信号中存在目标距离与方位角的耦合,本文提出利用这种耦合关系,采用M U S IC(M u ltip le S igna l C lassifica tion)算法获得目标距离以及方位角的超分辩,从而提高在多目标环境下测距、测角精度.仿真结果验证了该方法的有效性.
文摘提出了基于2q阶累积量的非圆信号测向MUSIC(Multiple Signal Classification)算法(称为NC-2q-MUSIC),作为2q-MUSIC算法利用非圆信息的一种扩展,在可测向信号数、分辨力和测角精度等方面的性能均优于2q-MUSIC算法.并且,q越大,NC-2q-MUSIC算法的可测向信号数越大,分辨力越高,对模型误差也越不敏感.针对均布线阵(ULA:Uniform Linear Array)提出的NC-2q-MUSIC/ULA算法减小了计算量.仿真实验验证了NC-2q-MUSIC算法的优良性能.
文摘由于MUSIC(MUltiple SIgnal Classification)算法需要大量的乘法运算和三角函数求值,导致其实时处理能力较弱。为此,该文首先对均匀线阵和均匀圆阵的阵列结构进行分析,提取导向矢量的一些性质。然后,利用Hermite矩阵的性质对复数乘法进行分解,再组建两个实值向量以减少乘法运算次数。最后,利用导向矢量的性质提出一种基于查表的新算法。新算法既没有三角函数求值运算,又不需要大量的存储空间。仿真实验结果表明新算法在没有改变MUSIC算法谱估计的效果的前提下,将MUSIC算法的运算速率提高了50倍以上。因此,新算法具有广阔的应用前景。
文摘Coalbed methane enrichment will be controlled by many good macro geological dynamical conditions; there is evident difference of enrichment grade in different area and different geological conditions.This paper has studied tectonic dynamical conditions, thermal dynamical conditions and hydraulic conditions, which affect coalbed methane enrichment in Qinshui basin.Coalbed methane enrichment units have been divided based on tectonic dynamical conditions of Qinshui basin,combined with thermal dynamical conditions and hydraulic conditions.
基金Project(KC18071)supported by the Application Foundation Research Program of Xuzhou,ChinaProjects(2017YFC0804401,2017YFC0804409)supported by the National Key R&D Program of China
文摘The sharp increase of the amount of Internet Chinese text data has significantly prolonged the processing time of classification on these data.In order to solve this problem,this paper proposes and implements a parallel naive Bayes algorithm(PNBA)for Chinese text classification based on Spark,a parallel memory computing platform for big data.This algorithm has implemented parallel operation throughout the entire training and prediction process of naive Bayes classifier mainly by adopting the programming model of resilient distributed datasets(RDD).For comparison,a PNBA based on Hadoop is also implemented.The test results show that in the same computing environment and for the same text sets,the Spark PNBA is obviously superior to the Hadoop PNBA in terms of key indicators such as speedup ratio and scalability.Therefore,Spark-based parallel algorithms can better meet the requirement of large-scale Chinese text data mining.
基金Projects(51822407,51774327,51664016)supported by the National Natural Science Foundation of China。
文摘Microseismic monitoring system is one of the effective methods for deep mining geo-stress monitoring.The principle of microseismic monitoring system is to analyze the mechanical parameters contained in microseismic events for providing accurate information of rockmass.The accurate identification of microseismic events and blasts determines the timeliness and accuracy of early warning of microseismic monitoring technology.An image identification model based on Convolutional Neural Network(CNN)is established in this paper for the seismic waveforms of microseismic events and blasts.Firstly,the training set,test set,and validation set are collected,which are composed of 5250,1500,and 750 seismic waveforms of microseismic events and blasts,respectively.The classified data sets are preprocessed and input into the constructed CNN in CPU mode for training.Results show that the accuracies of microseismic events and blasts are 99.46%and 99.33%in the test set,respectively.The accuracies of microseismic events and blasts are 100%and 98.13%in the validation set,respectively.The proposed method gives superior performance when compared with existed methods.The accuracies of models using logistic regression and artificial neural network(ANN)based on the same data set are 54.43%and 67.9%in the test set,respectively.Then,the ROC curves of the three models are obtained and compared,which show that the CNN gives an absolute advantage in this classification model when the original seismic waveform are used in training the model.It not only decreases the influence of individual differences in experience,but also removes the errors induced by source and waveform parameters.It is proved that the established discriminant method improves the efficiency and accuracy of microseismic data processing for monitoring rock instability and seismicity.
基金supported by the National Natural Science Foundation of China (60604021 60874054)
文摘To solve the multi-class fault diagnosis tasks, decision tree support vector machine （DTSVM）, which combines SVM and decision tree using the concept of dichotomy, is proposed. Since the classification performance of DTSVM highly depends on its structure, to cluster the multi-classes with maximum distance between the clustering centers of the two sub-classes, genetic algorithm is introduced into the formation of decision tree, so that the most separable classes would be separated at each node of decisions tree. Numerical simulations conducted on three datasets compared with ＂one-against-all＂ and ＂one-against-one＂ demonstrate the proposed method has better performance and higher generalization ability than the two conventional methods.
基金supported by the Academy of Finland(267581)the D2I SHOK Project from Digile Oy as well as Nokia Technologies(Tampere,Finland)
文摘Facial expression recognition is a hot topic in computer vision, but it remains challenging due to the feature inconsistency caused by person-specific 'characteristics of facial expressions. To address such a challenge, and inspired by the recent success of deep identity network (DeepID-Net) for face identification, this paper proposes a novel deep learning based framework for recognising human expressions with facial images. Compared to the existing deep learning methods, our proposed framework, which is based on multi-scale global images and local facial patches, can significantly achieve a better performance on facial expression recognition. Finally, we verify the effectiveness of our proposed framework through experiments on the public benchmarking datasets JAFFE and extended Cohn-Kanade (CK+).
