针对火灾发生时,火灾图像背景复杂、人工特征提取过程繁琐、对火灾图像的识别泛化能力不强、容易出现精度不高、误报和漏报等问题,提出了张量对象特征提取的多线性主成分分析(Multilinear Principal Component Analysis,MPCA)深度学习...针对火灾发生时,火灾图像背景复杂、人工特征提取过程繁琐、对火灾图像的识别泛化能力不强、容易出现精度不高、误报和漏报等问题,提出了张量对象特征提取的多线性主成分分析(Multilinear Principal Component Analysis,MPCA)深度学习算法的火灾图像识别新方法。利用MPCANet建立火灾图像识别模型,通过MPCA算法学习滤波器作为深度学习网络卷积层卷积核,对张量对象的高维图像进行特征提取,并把蜡烛图像和烟花图像作为干扰。通过仿真实验并与其他火灾图像识别方法对比得到提出的图像识别方法识别精度达到了97.5%、误报率1.5%、漏报率1%。实验表明:该方法可以有效解决火灾图像识别存在的问题。展开更多
提出了一种基于多层PCA网络(MPCANet)的深度学习模型来进行年龄估计.它是基于卷积神经网的结构来设计的,并且用来提取年龄特征.MPCANet是主成分分析网络(PCANet)的一种改进,它是最近提出的一种深度学习算法,MPCANet模型结构组成的成分:...提出了一种基于多层PCA网络(MPCANet)的深度学习模型来进行年龄估计.它是基于卷积神经网的结构来设计的,并且用来提取年龄特征.MPCANet是主成分分析网络(PCANet)的一种改进,它是最近提出的一种深度学习算法,MPCANet模型结构组成的成分:(1)卷积滤波层是采用多层级联主成分分析(PCA),(2)非线性层则采用二进制哈希,(3)特征抽取层使用直方图统计方法.使用核支持向量回归(K-SVR)进行估计年龄值.实验分别在两个数据库(FG-NET and MORPH)上进行,实验结果表明该方法比目前最新的方法表现得更好.展开更多
Motion recognition refers to the intelligent recognition of human motion using data collected from wearable sensors,which exceedingly has gained significant interest from both academic and industrial fields.However,te...Motion recognition refers to the intelligent recognition of human motion using data collected from wearable sensors,which exceedingly has gained significant interest from both academic and industrial fields.However,temporary-sudden activities caused by accidental behavior pose a major challenge to motion recognition and have been largely overlooked in existing works.To address this problem,the multi-dimensional time series of motion data is modeled as a Time-Frequency(TF)tensor,and the original challenge is transformed into a problem of outlier-corrupted tensor pattern recognition,where transient sudden activity data are considered as outliers.Since the TF tensor can capture the latent spatio-temporal correlations of the motion data,the tensor MPCA is used to derive the principal spatio-temporal pattern of the motion data.However,traditional MPCA uses the squared F-norm as the projection distance measure,which makes it sensitive to the presence of outlier motion data.Therefore,in the proposed outlier-robust MPCA scheme,the F-norm with the desirable geometric properties is used as the distance measure to simultaneously mitigate the interference of outlier motion data while preserving rotational invariance.Moreover,to reduce the complexity of outlier-robust motion recognition,we impose the proposed outlier-robust MPCA scheme on the traditional MPCANet which is a low-complexity deep learning network.The experimental results show that our proposed outlier-robust MPCANet can simultaneously improve motion recognition performance and reduce the complexity,especially in practical scenarios where the real-time data is corrupted by temporary-sudden activities.展开更多
文摘针对火灾发生时,火灾图像背景复杂、人工特征提取过程繁琐、对火灾图像的识别泛化能力不强、容易出现精度不高、误报和漏报等问题,提出了张量对象特征提取的多线性主成分分析(Multilinear Principal Component Analysis,MPCA)深度学习算法的火灾图像识别新方法。利用MPCANet建立火灾图像识别模型,通过MPCA算法学习滤波器作为深度学习网络卷积层卷积核,对张量对象的高维图像进行特征提取,并把蜡烛图像和烟花图像作为干扰。通过仿真实验并与其他火灾图像识别方法对比得到提出的图像识别方法识别精度达到了97.5%、误报率1.5%、漏报率1%。实验表明:该方法可以有效解决火灾图像识别存在的问题。
文摘提出了一种基于多层PCA网络(MPCANet)的深度学习模型来进行年龄估计.它是基于卷积神经网的结构来设计的,并且用来提取年龄特征.MPCANet是主成分分析网络(PCANet)的一种改进,它是最近提出的一种深度学习算法,MPCANet模型结构组成的成分:(1)卷积滤波层是采用多层级联主成分分析(PCA),(2)非线性层则采用二进制哈希,(3)特征抽取层使用直方图统计方法.使用核支持向量回归(K-SVR)进行估计年龄值.实验分别在两个数据库(FG-NET and MORPH)上进行,实验结果表明该方法比目前最新的方法表现得更好.
基金supported by the National Science Foundation of China under Grant No.62101467。
文摘Motion recognition refers to the intelligent recognition of human motion using data collected from wearable sensors,which exceedingly has gained significant interest from both academic and industrial fields.However,temporary-sudden activities caused by accidental behavior pose a major challenge to motion recognition and have been largely overlooked in existing works.To address this problem,the multi-dimensional time series of motion data is modeled as a Time-Frequency(TF)tensor,and the original challenge is transformed into a problem of outlier-corrupted tensor pattern recognition,where transient sudden activity data are considered as outliers.Since the TF tensor can capture the latent spatio-temporal correlations of the motion data,the tensor MPCA is used to derive the principal spatio-temporal pattern of the motion data.However,traditional MPCA uses the squared F-norm as the projection distance measure,which makes it sensitive to the presence of outlier motion data.Therefore,in the proposed outlier-robust MPCA scheme,the F-norm with the desirable geometric properties is used as the distance measure to simultaneously mitigate the interference of outlier motion data while preserving rotational invariance.Moreover,to reduce the complexity of outlier-robust motion recognition,we impose the proposed outlier-robust MPCA scheme on the traditional MPCANet which is a low-complexity deep learning network.The experimental results show that our proposed outlier-robust MPCANet can simultaneously improve motion recognition performance and reduce the complexity,especially in practical scenarios where the real-time data is corrupted by temporary-sudden activities.
