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Paper in IEEE CVPR 2013 “Decoding Children’s Social Behavior”

June 27th, 2013 Irfan Essa Posted in Affective Computing, Behavioral Imaging, Denis Lantsman, Gregory Abowd, James Rehg, PAMI/ICCV/CVPR/ECCV, Papers, Thomas Ploetz No Comments »

  • J. M. Rehg, G. D. Abowd, A. Rozga, M. Romero, M. A. Clements, S. Sclaroff, I. Essa, O. Y. Ousley, Y. Li, C. Kim, H. Rao, J. C. Kim, L. L. Presti, J. Zhang, D. Lantsman, J. Bidwell, and Z. Ye (2013), “Decoding Children’s Social Behavior,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013. [PDF] [WEBSITE] [DOI] [BIBTEX]
    @inproceedings{2013-Rehg-DCSB,
      Author = {James M. Rehg and Gregory D. Abowd and Agata Rozga and Mario Romero and Mark A. Clements and Stan Sclaroff and Irfan Essa and Opal Y. Ousley and Yin Li and Chanho Kim and Hrishikesh Rao and Jonathan C. Kim and Liliana Lo Presti and Jianming Zhang and Denis Lantsman and Jonathan Bidwell and Zhefan Ye},
      Booktitle = {Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
      Date-Added = {2013-06-25 11:47:42 +0000},
      Date-Modified = {2013-10-22 18:50:31 +0000},
      Doi = {10.1109/CVPR.2013.438},
      Month = {June},
      Organization = {IEEE Computer Society},
      Pdf = {http://www.cc.gatech.edu/~rehg/Papers/Rehg_CVPR13.pdf},
      Title = {Decoding Children's Social Behavior},
      Url = {http://www.cbi.gatech.edu/mmdb/},
      Year = {2013},
      Bdsk-Url-1 = {http://www.cbi.gatech.edu/mmdb/},
      Bdsk-Url-2 = {http://dx.doi.org/10.1109/CVPR.2013.438}}

Abstract

We introduce a new problem domain for activity recognition: the analysis of children’s social and communicative behaviors based on video and audio data. We specifically target interactions between children aged 1-2 years and an adult. Such interactions arise naturally in the diagnosis and treatment of developmental disorders such as autism. We introduce a new publicly-available dataset containing over 160 sessions of a 3-5 minute child-adult interaction. In each session, the adult examiner followed a semi-structured play interaction protocol which was designed to elicit a broad range of social behaviors. We identify the key technical challenges in analyzing these behaviors, and describe methods for decoding the interactions. We present experimental results that demonstrate the potential of the dataset to drive interesting research questions, and show preliminary results for multi-modal activity recognition.

Full database available from http://www.cbi.gatech.edu/mmdb/

via IEEE Xplore – Decoding Children’s Social Behavior.

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Paper in IEEE CVPR 2013 “Augmenting Bag-of-Words: Data-Driven Discovery of Temporal and Structural Information for Activity Recognition”

June 27th, 2013 Irfan Essa Posted in Activity Recognition, Behavioral Imaging, Grant Schindler, PAMI/ICCV/CVPR/ECCV, Papers, Sports Visualization, Thomas Ploetz, Vinay Bettadapura No Comments »

  • V. Bettadapura, G. Schindler, T. Ploetz, and I. Essa (2013), “Augmenting Bag-of-Words: Data-Driven Discovery of Temporal and Structural Information for Activity Recognition,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013. [PDF] [WEBSITE] [DOI] [BIBTEX]
    @inproceedings{2013-Bettadapura-ABDDTSIAR,
      Author = {Vinay Bettadapura and Grant Schindler and Thomas Ploetz and Irfan Essa},
      Booktitle = {Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
      Date-Added = {2013-06-25 11:42:31 +0000},
      Date-Modified = {2013-10-22 18:39:15 +0000},
      Doi = {10.1109/CVPR.2013.338},
      Month = {June},
      Organization = {IEEE Computer Society},
      Pdf = {http://www.cc.gatech.edu/~irfan/p/2013-Bettadapura-ABDDTSIAR.pdf},
      Title = {Augmenting Bag-of-Words: Data-Driven Discovery of Temporal and Structural Information for Activity Recognition},
      Url = {http://www.cc.gatech.edu/cpl/projects/abow/},
      Year = {2013},
      Bdsk-Url-1 = {http://www.cc.gatech.edu/cpl/projects/abow/},
      Bdsk-Url-2 = {http://dx.doi.org/10.1109/CVPR.2013.338}}

Abstract

We present data-driven techniques to augment Bag of Words (BoW) models, which allow for more robust modeling and recognition of complex long-term activities, especially when the structure and topology of the activities are not known a priori. Our approach specifically addresses the limitations of standard BoW approaches, which fail to represent the underlying temporal and causal information that is inherent in activity streams. In addition, we also propose the use of randomly sampled regular expressions to discover and encode patterns in activities. We demonstrate the effectiveness of our approach in experimental evaluations where we successfully recognize activities and detect anomalies in four complex datasets.

via IEEE Xplore – Augmenting Bag-of-Words: Data-Driven Discovery of Temporal and Structural Information for Activity R….

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Paper in AISTATS 2013 “Beyond Sentiment: The Manifold of Human Emotions”

April 29th, 2013 Irfan Essa Posted in AAAI/IJCAI/UAI, Behavioral Imaging, Computational Journalism, Numerical Machine Learning, Papers, WWW No Comments »

  • S. Kim, F. Li, G. Lebanon, and I. A. Essa (2013), “Beyond Sentiment: The Manifold of Human Emotions,” in Proceedings of AI STATS, 2013. [PDF] [BIBTEX]
    @inproceedings{2012-Kim-BSMHE,
      Author = {Seungyeon Kim and Fuxin Li and Guy Lebanon and Irfan A. Essa},
      Booktitle = {Proceedings of AI STATS},
      Date-Added = {2013-06-25 12:01:11 +0000},
      Date-Modified = {2013-06-25 12:02:53 +0000},
      Pdf = {http://arxiv.org/pdf/1202.1568v1},
      Title = {Beyond Sentiment: The Manifold of Human Emotions},
      Year = {2013}}

Abstract

Sentiment analysis predicts the presence of positive or negative emotions in a text document. In this paper we consider higher dimensional extensions of the sentiment concept, which represent a richer set of human emotions. Our approach goes beyond previous work in that our model contains a continuous manifold rather than a finite set of human emotions. We investigate the resulting model, compare it to psychological observations, and explore its predictive capabilities. Besides obtaining significant improvements over a baseline without manifold, we are also able to visualize different notions of positive sentiment in different domains.

via [arXiv.org 1202.1568] Beyond Sentiment: The Manifold of Human Emotions.

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