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	A hybrid-supervision learning algorithm for real-time un-completed face recognition 期刊论文 OAI收割 COMPUTERS & ELECTRICAL ENGINEERING, 2022, 卷号: 101, 页码: 17 作者: Zhao, Shuhuan; Liu, Wen; Liu, Shuaiqi; Ge, Jiaqi; Liang, Xiaolin \| 收藏 \| 浏览/下载：39/0 \| 提交时间：2022/11/14 Face recognition Feature fusion Hybrid supervised learning Multiple marginal Fisher analysis
	Industrial Weak Scratches Inspection Based on Multifeature Fusion Network 期刊论文 OAI收割 IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2021, 卷号: 70, 期号: 1, 页码: 14 作者: Tao, Xian; Zhang, Dapeng; Hou, Wei; Ma, Wenzhi; Xu, De \| 收藏 \| 浏览/下载：21/0 \| 提交时间：2021/03/08 Deep learning defect detection machine vision multiple feature fusion weak scratch inspection
	Shared Low-Rank Correlation Embedding for Multiple Feature Fusion 期刊论文 OAI收割 IEEE TRANSACTIONS ON MULTIMEDIA, 2021, 卷号: 23, 页码: 1855-1867 作者: Wang, Zhan; Wang, Lizhi; Wan, Jun; Huang, Hua \| 收藏 \| 浏览/下载：21/0 \| 提交时间：2021/12/28 Correlation Kernel Task analysis Fuses Noise measurement Laplace equations Dictionaries Common subspace low-rank representation multiple feature fusion canonical correlation analysis
	Visual Tracking Based on Multi-Feature and Fast Scale Adaptive Kernelized Correlation Filter 期刊论文 OAI收割 IEEE ACCESS, 2019, 卷号: 7, 页码: 83209-83228 作者: Zeng, Xianyou; Xu, Long; Cen, Yigang; Zhao, Ruizhen; Hu, Shaohai \| 收藏 \| 浏览/下载：34/0 \| 提交时间：2020/03/10 Visual tracking scale filter dimension reduction multiple feature fusion dynamic learning rate
	Fusing magnitude and phase features with multiple face models for robust face recognition 期刊论文 OAI收割 FRONTIERS OF COMPUTER SCIENCE, 2018, 卷号: 12, 期号: 6, 页码: 1173-1191 作者: Li, Yan; Wang, Ruiping; Cui, Zhen; Chen, Xilin; Shan, Shiguang \| 收藏 \| 浏览/下载：41/0 \| 提交时间：2019/04/03 face recognition fisher discriminant analysis fusion Gabor magnitude feature multiple face models spatial pyramid based local phase quantization
	fMRI classification method with multiple feature fusion based on minimum spanning tree analysis 期刊论文 OAI收割 PSYCHIATRY RESEARCH-NEUROIMAGING, 2018, 卷号: 277, 页码: 14-27 作者: Guo, Hao; Yan, Pengpeng; Cheng, Chen; Li, Yao; Chen, Junjie \| 收藏 \| 浏览/下载：24/0 \| 提交时间：2019/12/16 Functional brain network Minimum spanning tree Classifier Depression Multiple feature fusion
	The new approach for infrared target tracking based on the particle filter algorithm (EI CONFERENCE) 会议论文 OAI收割 International Symposium on Photoelectronic Detection and Imaging 2011: Advances in Infrared Imaging and Applications, May 24, 2011 - May 24, 2011, Beijing, China 作者: Sun H.; Han H.-X.; Sun H. 收藏 \| 浏览/下载：60/0 \| 提交时间：2013/03/25 Target tracking on the complex background in the infrared image sequence is hot research field. It provides the important basis in some fields such as video monitoring precision and video compression human-computer interaction. As a typical algorithms in the target tracking framework based on filtering and data connection the particle filter with non-parameter estimation characteristic have ability to deal with nonlinear and non-Gaussian problems so it were widely used. There are various forms of density in the particle filter algorithm to make it valid when target occlusion occurred or recover tracking back from failure in track procedure but in order to capture the change of the state space it need a certain amount of particles to ensure samples is enough and this number will increase in accompany with dimension and increase exponentially this led to the increased amount of calculation is presented. In this paper particle filter algorithm and the Mean shift will be combined. Aiming at deficiencies of the classic mean shift Tracking algorithm easily trapped into local minima and Unable to get global optimal under the complex background. From these two perspectives that "adaptive multiple information fusion" and "with particle filter framework combining" we expand the classic Mean Shift tracking framework.Based on the previous perspective we proposed an improved Mean Shift infrared target tracking algorithm based on multiple information fusion. In the analysis of the infrared characteristics of target basis Algorithm firstly extracted target gray and edge character and Proposed to guide the above two characteristics by the moving of the target information thus we can get new sports guide grayscale characteristics and motion guide border feature. Then proposes a new adaptive fusion mechanism used these two new information adaptive to integrate into the Mean Shift tracking framework. Finally we designed a kind of automatic target model updating strategy to further improve tracking performance. Experimental results show that this algorithm can compensate shortcoming of the particle filter has too much computation and can effectively overcome the fault that mean shift is easy to fall into local extreme value instead of global maximum value.Last because of the gray and fusion target motion information this approach also inhibit interference from the background ultimately improve the stability and the real-time of the target track. 2011 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE).
	Multiple-kernel SVM based multiple-task oriented data mining system for gene expression data analysis 期刊论文 OAI收割 EXPERT SYSTEMS WITH APPLICATIONS, 2011, 卷号: 38, 期号: 10, 页码: 9,12151-12159 Chen, ZY; Li, JP; Wei, LW; Xu, WX; Shi, Y 收藏 \| 浏览/下载：26/0 \| 提交时间：2012/11/12 Support vector machine Multiple-kernel learning Feature selection Data fusion Decision rule Associated rule Subclass discovery Gene expression
	基于局部特征的物体分类关键技术研究学位论文 OAI收割工学博士, 中国科学院自动化研究所: 中国科学院研究生院, 2010 作者: 欧阳毅收藏 \| 浏览/下载：75/0 \| 提交时间：2015/09/02 物体分类局部特征局部特征学习多特征融合 Object categorization Local feature Local feature learning Multiple feature fusion