Coloring action recognition in still images

Fahad Shahbaz Khan, Muhammad Anwer Rao, Joost Van De Weijer, Andrew D. Bagdanov, Antonio M. Lopez, Michael Felsberg

Research output: Contribution to journalArticleResearchpeer-review

102 Citations (Scopus)


In this article we investigate the problem of human action recognition in static images. By action recognition we intend a class of problems which includes both action classification and action detection (i.e. simultaneous localization and classification). Bag-of-words image representations yield promising results for action classification, and deformable part models perform very well object detection. The representations for action recognition typically use only shape cues and ignore color information. Inspired by the recent success of color in image classification and object detection, we investigate the potential of color for action classification and detection in static images. We perform a comprehensive evaluation of color descriptors and fusion approaches for action recognition. Experiments were conducted on the three datasets most used for benchmarking action recognition in still images: Willow, PASCAL VOC 2010 and Stanford-40. Our experiments demonstrate that incorporating color information considerably improves recognition performance, and that a descriptor based on color names outperforms pure color descriptors. Our experiments demonstrate that late fusion of color and shape information outperforms other approaches on action recognition. Finally, we show that the different color-shape fusion approaches result in complementary information and combining them yields state-of-the-art performance for action classification. © 2013 Springer Science+Business Media New York.
Original languageEnglish
Pages (from-to)205-221
JournalInternational Journal of Computer Vision
Issue number3
Publication statusPublished - 1 Dec 2013


  • Action recognition
  • Color features
  • Image representation


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