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Grouplet：用于识别人与对象交互的结构化图像表示
Grouplet: A Structured Image Representation for Recognizing Human and Object Interactions |
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| 课程网址: |
http://videolectures.net/cvpr2010_yao_gsir/
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| 主讲教师: |
Bangpeng Yao
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| 开课单位: |
斯坦福大学
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| 开课时间: |
2010-07-19
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| 课程语种: |
英语
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| 中文简介: |
心理学家已经提出许多人类对象交互活动形成独特的场景类别。认识到这些场景对许多社交功能都很重要。然而，要让计算机执行此操作是一项具有挑战性的任务。以人们演奏乐器（PPMI）为例;区分拉小提琴的人与拿着小提琴的人只需要微妙区分特征图像特征和区分这两个场景的特征排列。大多数现有的图像表示方法要么太粗糙（例如BoW），要么稀疏（例如，星座模型）用于执行该任务。在本文中，我们提出了一种新的图像特征表示，称为“grouplet”。组合通过编码多个有辨别力的视觉特征及其空间配置来捕获图像的结构化信息。使用7种不同PPMI活动的数据集，我们表明组合比人类方法的其他状态更有效地分类和检测人类对象交互。特别是，我们的方法可以在人类演奏乐器和使用乐器演奏的人类之间进行强有力的分辨，而无需演奏。
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| 课程简介: |
Psychologists have proposed that many human-object interaction activities form unique classes of scenes. Recognizing these scenes is important for many social functions. To enable a computer to do this is however a challenging task. Take people-playing-musical-instrument (PPMI) as an example; to distinguish a person playing violin from a person just holding a violin requires subtle distinction of characteristic image features and feature arrangements that differentiate these two scenes. Most of the existing image representation methods are either too coarse (e.g. BoW) or too sparse (e.g. constellation models) for performing this task. In this paper, we propose a new image feature representation called “grouplet”. The grouplet captures the structured information of an image by encoding a number of discriminative visual features and their spatial configurations. Using a dataset of 7 different PPMI activities, we show that grouplets are more effective in classifying and detecting human-object interactions than other state-of-theart methods. In particular, our method can make a robust distinction between humans playing the instruments and humans co-occurring with the instruments without playing.
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| 关 键 词: |
交互活动; 社交功能; 视觉特征
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| 课程来源: |
视频讲座网
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| 最后编审: |
2019-03-13：lxf
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| 阅读次数: |
49
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