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摘要:
This paper proposes an efficient and simple method for identity recognition in uncontrolled videos. The idea is to use images collected from the web to learn representations of actions related with identity, use this knowledge to automatically annotate identity in videos. Our approach is unsupervised where it can identify the identity of human in the video like YouTube directly through the knowledge of his actions. Its benefits are two-fold: 1) we can improve retrieval of identity images, and 2) we can collect a database of action poses related with identity, which can then be used in tagging videos. We present the simple experimental evidence that using action images related with identity collected from the web, annotating identity is possible.
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篇名 Learning Actions from the Identity in the Web
来源期刊 电脑和通信(英文) 学科 医学
关键词 Action RECOGNITION HOG SVM Classification
年,卷(期) 2014,(9) 所属期刊栏目
研究方向 页码范围 54-60
页数 7页 分类号 R73
字数 语种
DOI
五维指标
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Action
RECOGNITION
HOG
SVM
Classification
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相关学者/机构
期刊影响力
电脑和通信(英文)
月刊
2327-5219
武汉市江夏区汤逊湖北路38号光谷总部空间
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783
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