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	WavDepressionNet: Automatic Depression Level Prediction via Raw Speech Signals 期刊论文 OAI收割 IEEE TRANSACTIONS ON AFFECTIVE COMPUTING, 2024, 卷号: 15, 期号: 1, 页码: 285-296 作者: Niu, Mingyue; Tao, Jianhua; Li, Yongwei; Qin, Yong; Li, Ya \| 收藏 \| 浏览/下载：28/0 \| 提交时间：2024/07/03 Assessment block depression level prediction representation block speech signals WavDepressionNet
	Modal-Regression-Based Structured Low-Rank Matrix Recovery for Multiview Learning 期刊论文 OAI收割 IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2021, 卷号: 32, 期号: 3, 页码: 1204-1216 作者: Xu, Jiamiao; Wang, Fangzhao; Peng, Qinmu; You, Xinge; Wang, Shuo \| 收藏 \| 浏览/下载：44/0 \| 提交时间：2021/04/27 Robustness Laplace equations Technological innovation Learning systems Data models Gaussian noise Convex functions Block-diagonal representation learning cross-view classification low-rank representation multiview learning
	Locality and Structure Regularized Low Rank Representation for Hyperspectral Image Classification 期刊论文 OAI收割 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2019, 卷号: 57, 期号: 2, 页码: 911-923 作者: Wang, Qi; He, Xiang; Li, Xuelong \| 收藏 \| 浏览/下载：53/0 \| 提交时间：2019/03/14 Block-diagonal structure hyperspectral image (HSI) classification low-rank representation (LRR)
	Learning Robust Face Representation With Classwise Block-Diagonal Structure 期刊论文 OAI收割 IEEE Transactions on Information Forensics and Security, 2014, 卷号: 9, 期号: 12, 页码: 2051-2062 作者: Li, Yong; Liu, Jing; Lu, Hanqing; Ma, Songde 收藏 \| 浏览/下载：36/0 \| 提交时间：2015/09/21 Robust face recognition low-rank and sparse representation classwise block-diagonal structure
	Block covariance based l(1) tracker with a subtle template dictionary 期刊论文 OAI收割 PATTERN RECOGNITION, 2013, 卷号: 46, 期号: 7, 页码: 1750-1761 作者: Zhang, Xiaoqin; Li, Wei; Hu, Weiming; Ling, Haibin; Maybank, Steve 收藏 \| 浏览/下载：19/0 \| 提交时间：2015/08/12 Visual tracking Sparse representation Block division Covariance feature Template update
	The Application of Wavelet-Based Contourlet Transform on Compressed Sensing 会议论文 OAI收割 2012 International Conference on Multimedia and Signal Processing, Shanghai, China, December 7-9, 2012 作者: Du M(杜梅); Zhao HC(赵怀慈); Zhao CY(赵春阳); Li B(李波) 收藏 \| 浏览/下载：91/0 \| 提交时间：2012/12/28 Sparse Representation Wavelet-Based Contourlet Transform Block Compressed Sensing Iterative Hard Thresholding Algorithm
	Local variance based color image quality assessment method (EI CONFERENCE) 会议论文 OAI收割 2011 2nd International Conference on Advanced Measurement and Test, AMT 2011, June 24, 2011 - June 26, 2011, Nanchang, China 作者: Wang Y.; Wang Y.; Wang Y.; Wang Y.; Wang Y. 收藏 \| 浏览/下载：28/0 \| 提交时间：2013/03/25 In this paper local variance is used to describe the structural information of a color image in order to assess its quality. The representation method is different from conventional models in that some information that is sensitive to human eyes is enhanced by using local variance distribution. It encodes the local variance distribution of different channels of a color image into the three imaginary parts of a quaternion. The distance between the singular value feature vectors of the source image block and the distorted image block which are described by quaternion matrices is calculated. The experimental results show that the assessment results of the proposed assessment method are more consistent with the Human Visual System than those of the conventional assessment methods.
	Color image quality assessment based on quaternion representation for the local variance distribution of RGB Channels (EI CONFERENCE) 会议论文 OAI收割 2009 2nd International Congress on Image and Signal Processing, CISP'09, October 17, 2009 - October 19, 2009, Tianjin, China Yuqing W.; Ming Z. 收藏 \| 浏览/下载：26/0 \| 提交时间：2013/03/25 In this paper Quaternions are used to describe the structural information of a color image in order to assess its quality. The representation method is different from conventional models in that some information that is sensitive to human eyes is reinforced by using local variance. It encodes the local variance distribution of the RGB channels of a color image into the three imaginary parts of a quaternion. The luminance layer of the color image is taken as the real part of the quaternion. The distance between the singular value feature vectors of the source image block and the distorted image block which are described by quaternion matrices is calculated. The final assessment result is calculated by using the mid point of the distances. The experimental results show that the assessment results of the proposed assessment method are more consistent with the Human Visual System than those of the conventional assessment methods. 2009 IEEE.