Deep and Structured Robust Information Theoretic Learning for Image Analysis
文献类型：期刊论文
| 作者 | Deng, Yue1,2; Bao, Feng1; Deng, Xuesong3; Wang, Ruiping3; Kong, Youyong4; Dai, Qionghai1 |
| 刊名 | IEEE TRANSACTIONS ON IMAGE PROCESSING
 |
| 出版日期 | 2016-09-01 |
| 卷号 | 25期号:9页码:4209-4221 |
| 关键词 | Data embedding mutual information deep learning structured-sparse learning image classification brain MRI segmentation |
| ISSN号 | 1057-7149 |
| DOI | 10.1109/TIP.2016.2588330 |
| 英文摘要 | This paper presents a robust information theoretic (RIT) model to reduce the uncertainties, i.e., missing and noisy labels, in general discriminative data representation tasks. The fundamental pursuit of our model is to simultaneously learn a transformation function and a discriminative classifier that maximize the mutual information of data and their labels in the latent space. In this general paradigm, we, respectively, discuss three types of the RIT implementations with linear subspace embedding, deep transformation, and structured sparse learning. In practice, the RIT and deep RIT are exploited to solve the image categorization task whose performances will be verified on various benchmark data sets. The structured sparse RIT is further applied to a medical image analysis task for brain magnetic resonance image segmentation that allows group-level feature selections on the brain tissues. |
| 资助项目 | National Natural Science Foundation of China[61327902] ; National Natural Science Foundation of China[61120106003] ; National Natural Science Foundation of China[61379083] ; National Science Foundation of Jiangsu Province, China[BK20150650] |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000380355600005 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/8249]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Deng, Yue |
| 作者单位 | 1.Tsinghua Univ, Automat Dept, Tsinghua Natl Lab Informat Sci & Technol, Beijing 100084, Peoples R China 2.Univ Calif San Francisco, San Francisco Med Ctr, San Francisco, CA 94158 USA 3.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China 4.Southeast Univ, Sch Comp Sci & Engn, Nanjing 210000, Jiangsu, Peoples R China |
| 推荐引用方式 GB/T 7714 | Deng, Yue,Bao, Feng,Deng, Xuesong,et al. Deep and Structured Robust Information Theoretic Learning for Image Analysis[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2016,25(9):4209-4221. |
| APA | Deng, Yue,Bao, Feng,Deng, Xuesong,Wang, Ruiping,Kong, Youyong,&Dai, Qionghai.(2016).Deep and Structured Robust Information Theoretic Learning for Image Analysis.IEEE TRANSACTIONS ON IMAGE PROCESSING,25(9),4209-4221. |
| MLA | Deng, Yue,et al."Deep and Structured Robust Information Theoretic Learning for Image Analysis".IEEE TRANSACTIONS ON IMAGE PROCESSING 25.9(2016):4209-4221. |
入库方式： OAI收割
来源：计算技术研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。