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Relative Forest for Visual Attribute Prediction

文献类型：期刊论文

作者Li, Shaoxin1; Shan, Shiguang2; Yan, Shuicheng3; Chen, Xilin2
刊名IEEE TRANSACTIONS ON IMAGE PROCESSING
出版日期2016-09-01
卷号25期号:9页码:13
关键词Visual attributes relative attributes random forest relative forest
ISSN号1057-7149
DOI10.1109/TIP.2016.2580939
英文摘要Accurate prediction of the visual attributes is significant in various recognition tasks. For many visual attributes, while it is very difficult to describe the exact degrees of their presences, by comparing the pairs of samples, the relative ordering of presences may be easily figured out. Based on this observation, instead of considering such attribute as binary attribute, the relative attribute method learns a ranking function for each attribute to provide more accurate and informative prediction results. In this paper, we also explore pairwise ranking for visual attribute prediction and propose to improve the relative attribute method in two aspects. First, we propose a relative tree method, which can achieve more accurate ranking in case of nonlinearly distributed visual data. Second, by resorting to randomization and ensemble learning, the relative tree method is extended to the relative forest method to further boost the accuracy and simultaneously reduce the computational cost. To validate the effectiveness of the proposed methods, we conduct extensive experiments on four databases: PubFig, OSR, FGNET, and WebFace. The results show that the proposed relative forest method not only outperforms the original relative attribute method, but also achieve the state-of-the-art accuracy for ordinal visual attribute prediction.
资助项目973 Program[2015CB351802] ; Natural Science Foundation of China[61390511] ; Natural Science Foundation of China[61402443] ; Strategic Priority Research Program through the Chinese Academy of Sciences[XDB02070004]
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000379900300003
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://119.78.100.204/handle/2XEOYT63/8268]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Shan, Shiguang
作者单位1.Tencent YouTu Lab, Tencent Shanghai 200233, Peoples R China
2.Chinese Acad Sci, Key Lab Intelligent Informat Proc, Inst Comp Technol, Beijing 100190, Peoples R China
3.360 AI Inst, Beijing 100015, Peoples R China
推荐引用方式
GB/T 7714		 Li, Shaoxin,Shan, Shiguang,Yan, Shuicheng,et al. Relative Forest for Visual Attribute Prediction[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2016,25(9):13.
APA Li, Shaoxin,Shan, Shiguang,Yan, Shuicheng,&Chen, Xilin.(2016).Relative Forest for Visual Attribute Prediction.IEEE TRANSACTIONS ON IMAGE PROCESSING,25(9),13.
MLA Li, Shaoxin,et al."Relative Forest for Visual Attribute Prediction".IEEE TRANSACTIONS ON IMAGE PROCESSING 25.9(2016):13.

入库方式： OAI收割

来源：计算技术研究所

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