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      • Department of Computer Engineering
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      On recognizing actions in still images via multiple features

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      Author(s)
      Şener, Fadime
      Bas, C.
      Ikizler-Cinbis, N.
      Date
      2012
      Source Title
      Computer Vision – ECCV 2012. Workshops and Demonstrations
      Print ISSN
      0302-9743
      Publisher
      Springer, Berlin, Heidelberg
      Volume
      3
      Pages
      263 - 272
      Language
      English
      Type
      Conference Paper
      Item Usage Stats
      195
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      268
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      Abstract
      We propose a multi-cue based approach for recognizing human actions in still images, where relevant object regions are discovered and utilized in a weakly supervised manner. Our approach does not require any explicitly trained object detector or part/attribute annotation. Instead, a multiple instance learning approach is used over sets of object hypotheses in order to represent objects relevant to the actions. We test our method on the extensive Stanford 40 Actions dataset [1] and achieve significant performance gain compared to the state-of-the-art. Our results show that using multiple object hypotheses within multiple instance learning is effective for human action recognition in still images and such an object representation is suitable for using in conjunction with other visual features. © 2012 Springer-Verlag.
      Keywords
      Data sets
      Human actions
      Human-action recognition
      Multiple features
      Multiple instance learning
      Multiple objects
      Object detectors
      Object region
      Object representations
      Performance Gain
      Stanford
      Still images
      Visual feature
      Gesture recognition
      Learning systems
      Statistical tests
      Computer vision
      Permalink
      http://hdl.handle.net/11693/28150
      Published Version (Please cite this version)
      http://dx.doi.org/10.1007/978-3-642-33885-4_27
      https://doi.org/10.1007/978-3-642-33885-4
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      • Department of Computer Engineering 1435
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