Paper in AAAI’s ICWSM (2017) “Selfie-Presentation in Everyday Life: A Large-Scale Characterization of Selfie Contexts on Instagram”

May 18th, 2017 Irfan Essa Posted in Computational Journalism, Computational Photography and Video, Computer Vision, Face and Gesture, Julia Deeb-Swihart, Papers, Social Computing No Comments »

Paper

  • J. Deeb-Swihart, C. Polack, E. Gilbert, and I. Essa (2017), “Selfie-Presentation in Everyday Life: A Large-Scale Characterization of Selfie Contexts on Instagram,” in In Proceedings of The International AAAI Conference on Web and Social Media (ICWSM), 2017. [PDF] [BIBTEX]
    @InProceedings{    2017-Deeb-Swihart-SELLCSCI,
      author  = {Julia Deeb-Swihart and Christopher Polack and Eric
          Gilbert and Irfan Essa},
      booktitle  = {In Proceedings of The International AAAI Conference
          on Web and Social Media (ICWSM)},
      month    = {May},
      organization  = {AAAI},
      pdf    = {http://www.cc.gatech.edu/~irfan/p/2017-Deeb-Swihart-SELLCSCI.pdf},
      title    = {Selfie-Presentation in Everyday Life: A Large-Scale
          Characterization of Selfie Contexts on Instagram},
      year    = {2017}
    }

Abstract

Carefully managing the presentation of self via technology is a core practice on all modern social media platforms. Recently, selfies have emerged as a new, pervasive genre of identity performance. In many ways unique, selfies bring us full circle to Goffman—blending the online and offline selves together. In this paper, we take an empirical, Goffman-inspired look at the phenomenon of selfies. We report a large-scale, mixed-method analysis of the categories in which selfies appear on Instagram—an online community comprising over 400M people. Applying computer vision and network analysis techniques to 2.5M selfies, we present a typology of emergent selfie categories which represent emphasized identity statements. To the best of our knowledge, this is the first large-scale, empirical research on selfies. We conclude, contrary to common portrayals in the press, that selfies are really quite ordinary: they project identity signals such as wealth, health and physical attractiveness common to many online media, and to offline life.

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Paper in IJCNN (2017) “Towards Using Visual Attributes to Infer Image Sentiment Of Social Events”

May 18th, 2017 Irfan Essa Posted in Computational Journalism, Computational Photography and Video, Computer Vision, Machine Learning, Papers, Unaiza Ahsan No Comments »

Paper

  • U. Ahsan, M. D. Choudhury, and I. Essa (2017), “Towards Using Visual Attributes to Infer Image Sentiment Of Social Events,” in Proceedings of The International Joint Conference on Neural Networks, Anchorage, Alaska, US, 2017. [PDF] [BIBTEX]
    @InProceedings{    2017-Ahsan-TUVAIISSE,
      address  = {Anchorage, Alaska, US},
      author  = {Unaiza Ahsan and Munmun De Choudhury and Irfan
          Essa},
      booktitle  = {Proceedings of The International Joint Conference
          on Neural Networks},
      month    = {May},
      pdf    = {http://www.cc.gatech.edu/~irfan/p/2017-Ahsan-TUVAIISSE.pdf},
      publisher  = {International Neural Network Society},
      title    = {Towards Using Visual Attributes to Infer Image
          Sentiment Of Social Events},
      year    = {2017}
    }

Abstract

Widespread and pervasive adoption of smartphones has led to instant sharing of photographs that capture events ranging from mundane to life-altering happenings. We propose to capture sentiment information of such social event images leveraging their visual content. Our method extracts an intermediate visual representation of social event images based on the visual attributes that occur in the images going beyond
sentiment-specific attributes. We map the top predicted attributes to sentiments and extract the dominant emotion associated with a picture of a social event. Unlike recent approaches, our method generalizes to a variety of social events and even to unseen events, which are not available at training time. We demonstrate the effectiveness of our approach on a challenging social event image dataset and our method outperforms state-of-the-art approaches for classifying complex event images into sentiments.

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Paper in IEEE WACV (2017): “Complex Event Recognition from Images with Few Training Examples”

March 27th, 2017 Irfan Essa Posted in Computational Journalism, Computational Photography and Video, Computer Vision, PAMI/ICCV/CVPR/ECCV, Papers, Unaiza Ahsan No Comments »

Paper

  • U. Ahsan, C. Sun, J. Hays, and I. Essa (2017), “Complex Event Recognition from Images with Few Training Examples,” in IEEE Winter Conference on Applications of Computer Vision (WACV), 2017. [PDF] [arXiv] [BIBTEX]
    @InProceedings{    2017-Ahsan-CERFIWTE,
      arxiv    = {https://arxiv.org/abs/1701.04769},
      author  = {Unaiza Ahsan and Chen Sun and James Hays and Irfan
          Essa},
      booktitle  = {IEEE Winter Conference on Applications of Computer
          Vision (WACV)},
      month    = {March},
      pdf    = {http://www.cc.gatech.edu/~irfan/p/2017-Ahsan-CERFIWTE.pdf},
      title    = {Complex Event Recognition from Images with Few
          Training Examples},
      year    = {2017}
    }

Abstract

We propose to leverage concept-level representations for complex event recognition in photographs given limited training examples. We introduce a novel framework to discover event concept attributes from the web and use that to extract semantic features from images and classify them into social event categories with few training examples. Discovered concepts include a variety of objects, scenes, actions and event subtypes, leading to a discriminative and compact representation for event images. Web images are obtained for each discovered event concept and we use (pre-trained) CNN features to train concept classifiers. Extensive experiments on challenging event datasets demonstrate that our proposed method outperforms several baselines using deep CNN features directly in classifying images into events with limited training examples. We also demonstrate that our method achieves the best overall accuracy on a data set with unseen event categories using a single training example.

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Paper in M2CAI (workshop MICCAI) on “Fine-tuning Deep Architectures for Surgical Tool Detection” and results of Tool Detection Challange

October 21st, 2016 Irfan Essa Posted in Aneeq Zia, Awards, Computer Vision, Daniel Castro, Medical, MICCAI No Comments »

Paper

  • A. Zia, D. Castro, and I. Essa (2016), “Fine-tuning Deep Architectures for Surgical Tool Detection,” in Workshop and Challenges on Modeling and Monitoring of Computer Assisted Interventions (M2CAI), Held in Conjunction with International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Athens, Greece, 2016. [PDF] [WEBSITE] [BIBTEX]
    @InProceedings{    2016-Zia-FDASTD,
      address  = {Athens, Greece},
      author  = {Aneeq Zia and Daniel Castro and Irfan Essa},
      booktitle  = {Workshop and Challenges on Modeling and Monitoring
          of Computer Assisted Interventions (M2CAI), Held in
          Conjunction with International Conference on Medical
          Image Computing and Computer Assisted Intervention
          (MICCAI)},
      month    = {October},
      pdf    = {http://www.cc.gatech.edu/~irfan/p/2016-Zia-FDASTD.pdf},
      title    = {Fine-tuning Deep Architectures for Surgical Tool
          Detection},
      url    = {http://www.cc.gatech.edu/cpl/projects/deepm2cai/},
      year    = {2016}
    }

Abstract

Visualization of some of the training videos.

Understanding surgical workflow has been a key concern of the medical research community. One of the main advantages of surgical workflow detection is real-time operating room (OR) scheduling. For hospitals, each minute of OR time is important in order to reduce cost and increase patient throughput. Traditional approaches in this field generally tackle the video analysis using hand-crafted video features to facilitate the tool detection. Recently, Twinanda et al. presented a CNN architecture ’EndoNet’ which outperformed previous methods for both surgical tool detection and surgical phase detection. Given the recent success of these networks, we present a study of various architectures coupled with a submission to the M2CAI Surgical Tool Detection challenge. We achieved a top-3 result for the M2CAI competition with a mAP of 37.6.

 

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Paper (ACM MM 2016) “Leveraging Contextual Cues for Generating Basketball Highlights”

October 18th, 2016 Irfan Essa Posted in ACM MM, Caroline Pantofaru, Computational Photography and Video, Computer Vision, Papers, Sports Visualization, Vinay Bettadapura No Comments »

Paper

  • V. Bettadapura, C. Pantofaru, and I. Essa (2016), “Leveraging Contextual Cues for Generating Basketball Highlights,” in Proceedings of ACM International Conference on Multimedia (ACM-MM), 2016. [PDF] [WEBSITE] [arXiv] [BIBTEX]
    @InProceedings{    2016-Bettadapura-LCCGBH,
      arxiv    = {http://arxiv.org/abs/1606.08955},
      author  = {Vinay Bettadapura and Caroline Pantofaru and Irfan
          Essa},
      booktitle  = {Proceedings of ACM International Conference on
          Multimedia (ACM-MM)},
      month    = {October},
      organization  = {ACM},
      pdf    = {http://www.cc.gatech.edu/~irfan/p/2016-Bettadapura-LCCGBH.pdf},
      title    = {Leveraging Contextual Cues for Generating
          Basketball Highlights},
      url    = {http://www.vbettadapura.com/highlights/basketball/index.htm},
      year    = {2016}
    }

Abstract

2016-Bettadapura-LCCGBH

Leveraging Contextual Cues for Generating Basketball Highlights

The massive growth of sports videos has resulted in a need for automatic generation of sports highlights that are comparable in quality to the hand-edited highlights produced by broadcasters such as ESPN. Unlike previous works that mostly use audio-visual cues derived from the video, we propose an approach that additionally leverages contextual cues derived from the environment that the game is being played in. The contextual cues provide information about the excitement levels in the game, which can be ranked and selected to automatically produce high-quality basketball highlights. We introduce a new dataset of 25 NCAA games along with their play-by-play stats and the ground-truth excitement data for each basket. We explore the informativeness of five different cues derived from the video and from the environment through user studies. Our experiments show that for our study participants, the highlights produced by our system are comparable to the ones produced by ESPN for the same games.

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Paper in IJCARS (2016) on “Automated video-based assessment of surgical skills for training and evaluation in medical schools”

September 2nd, 2016 Irfan Essa Posted in Activity Recognition, Aneeq Zia, Computer Vision, Eric Sarin, Mark Clements, Medical, MICCAI, Thomas Ploetz, Vinay Bettadapura, Yachna Sharma No Comments »

Paper

  • A. Zia, Y. Sharma, V. Bettadapura, E. L. Sarin, T. Ploetz, M. A. Clements, and I. Essa (2016), “Automated video-based assessment of surgical skills for training and evaluation in medical schools,” International Journal of Computer Assisted Radiology and Surgery, vol. 11, iss. 9, pp. 1623-1636, 2016. [WEBSITE] [DOI] [BIBTEX]
    @Article{    2016-Zia-AVASSTEMS,
      author  = {Zia, Aneeq and Sharma, Yachna and Bettadapura,
          Vinay and Sarin, Eric L and Ploetz, Thomas and
          Clements, Mark A and Essa, Irfan},
      doi    = {10.1007/s11548-016-1468-2},
      journal  = {International Journal of Computer Assisted
          Radiology and Surgery},
      month    = {September},
      number  = {9},
      pages    = {1623--1636},
      publisher  = {Springer Berlin Heidelberg},
      title    = {Automated video-based assessment of surgical skills
          for training and evaluation in medical schools},
      url    = {http://link.springer.com/article/10.1007/s11548-016-1468-2},
      volume  = {11},
      year    = {2016}
    }

Abstract

2016-Zia-AVASSTEMS

Sample frames from our video dataset

Purpose: Routine evaluation of basic surgical skills in medical schools requires considerable time and effort from supervising faculty. For each surgical trainee, a supervisor has to observe the trainees in- person. Alternatively, supervisors may use training videos, which reduces some of the logistical overhead. All these approaches, however, are still incredibly time consuming and involve human bias. In this paper, we present an automated system for surgical skills assessment by analyzing video data of surgical activities.

Method : We compare different techniques for video-based surgical skill evaluation. We use techniques that capture the motion information at a coarser granularity using symbols or words, extract motion dynamics using textural patterns in a frame kernel matrix, and analyze fine-grained motion information using frequency analysis. Results: We were successfully able to classify surgeons into different skill levels with high accuracy. Our results indicate that fine-grained analysis of motion dynamics via frequency analysis is most effective in capturing the skill relevant information in surgical videos.

Conclusion: Our evaluations show that frequency features perform better than motion texture features, which in turn perform better than symbol/word-based features. Put succinctly, skill classification accuracy is positively correlated with motion granularity as demonstrated by our results on two challenging video datasets.

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Research Blog: Motion Stills – Create beautiful GIFs from Live Photos

June 7th, 2016 Irfan Essa Posted in Computational Photography and Video, Computer Vision, In The News, Interesting, Matthias Grundmann, Projects No Comments »

Kudos to the team from Machine Perception at Google Research that just launched the Motion Still App to generate novel photos on an iOS device. This work is in part aimed at combining efforts like Video Textures and Video Stabilization and a lot more.

Today we are releasing Motion Stills, an iOS app from Google Research that acts as a virtual camera operator for your Apple Live Photos. We use our video stabilization technology to freeze the background into a still photo or create sweeping cinematic pans. The resulting looping GIFs and movies come alive, and can easily be shared via messaging or on social media.

Source: Research Blog: Motion Stills – Create beautiful GIFs from Live Photos

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Paper (WACV 2016) “Discovering Picturesque Highlights from Egocentric Vacation Videos”

March 7th, 2016 Irfan Essa Posted in Computational Photography and Video, Computer Vision, Daniel Castro, PAMI/ICCV/CVPR/ECCV, Vinay Bettadapura No Comments »

Paper

  • D. Castro, V. Bettadapura, and I. Essa (2016), “Discovering Picturesque Highlights from Egocentric Vacation Video,” in Proceedings of IEEE Winter Conference on Applications of Computer Vision (WACV), 2016. [PDF] [WEBSITE] [arXiv] [BIBTEX]
    @InProceedings{    2016-Castro-DPHFEVV,
      arxiv    = {http://arxiv.org/abs/1601.04406},
      author  = {Daniel Castro and Vinay Bettadapura and Irfan
          Essa},
      booktitle  = {Proceedings of IEEE Winter Conference on
          Applications of Computer Vision (WACV)},
      month    = {March},
      pdf    = {http://www.cc.gatech.edu/~irfan/p/2016-Castro-DPHFEVV.pdf},
      title    = {Discovering Picturesque Highlights from Egocentric
          Vacation Video},
      url    = {http://www.cc.gatech.edu/cpl/projects/egocentrichighlights/},
      year    = {2016}
    }

Abstract

2016-Castro-DPHFEVVWe present an approach for identifying picturesque highlights from large amounts of egocentric video data. Given a set of egocentric videos captured over the course of a vacation, our method analyzes the videos and looks for images that have good picturesque and artistic properties. We introduce novel techniques to automatically determine aesthetic features such as composition, symmetry, and color vibrancy in egocentric videos and rank the video frames based on their photographic qualities to generate highlights. Our approach also uses contextual information such as GPS, when available, to assess the relative importance of each geographic location where the vacation videos were shot. Furthermore, we specifically leverage the properties of egocentric videos to improve our highlight detection. We demonstrate results on a new egocentric vacation dataset which includes 26.5 hours of videos taken over a 14-day vacation that spans many famous tourist destinations and also provide results from a user-study to access our results.

 

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Paper in MICCAI (2015): “Automated Assessment of Surgical Skills Using Frequency Analysis”

October 6th, 2015 Irfan Essa Posted in Activity Recognition, Aneeq Zia, Eric Sarin, Mark Clements, Medical, MICCAI, Papers, Vinay Bettadapura, Yachna Sharma No Comments »

Paper

  • A. Zia, Y. Sharma, V. Bettadapura, E. Sarin, M. Clements, and I. Essa (2015), “Automated Assessment of Surgical Skills Using Frequency Analysis,” in International Conference on Medical Image Computing and Computer Assisted Interventions (MICCAI), 2015. [PDF] [BIBTEX]
    @InProceedings{    2015-Zia-AASSUFA,
      author  = {A. Zia and Y. Sharma and V. Bettadapura and E.
          Sarin and M. Clements and I. Essa},
      booktitle  = {International Conference on Medical Image Computing
          and Computer Assisted Interventions (MICCAI)},
      month    = {October},
      pdf    = {http://www.cc.gatech.edu/~irfan/p/2015-Zia-AASSUFA.pdf},
      title    = {Automated Assessment of Surgical Skills Using
          Frequency Analysis},
      year    = {2015}
    }

Abstract

We present an automated framework for a visual assessment of the expertise level of surgeons using the OSATS (Objective Structured Assessment of Technical Skills) criteria. Video analysis technique for extracting motion quality via  frequency coefficients is introduced. The framework is tested in a case study that involved analysis of videos of medical students with different expertise levels performing basic surgical tasks in a surgical training lab setting. We demonstrate that transforming the sequential time data into frequency components effectively extracts the useful information differentiating between different skill levels of the surgeons. The results show significant performance improvements using DFT and DCT coefficients over known state-of-the-art techniques.

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2015 C+J Symposium

October 2nd, 2015 Irfan Essa Posted in Computational Journalism, Nick Diakopoulos No Comments »

logoData and computation drive our world, often without sufficient critical assessment or accountability. Journalism is adapting responsibly—finding and creating new kinds of stories that respond directly to our new societal condition. Join us for a two-day conference exploring the interface between journalism and computing.October 2-3, New York, NY#CJ2015

Source: 2015 C+J Symposium

Participated the 4th Computation+Journalism Symposium, October 2-3, in New York, NY at The Brown Institute for Media Innovation Pulitzer Hall, Columbia University.  Keynotes were Lada Adamic (Facebook) and Chris Wiggins (Columbia, NYT), with 2 curated panels and 5 sessions of peer-reviewed papers.

Past Symposiums were held in

  • Atlanta, GA (CJ 2008, hosted by Georgia Tech),
  • Atlanta, GA (CJ 2013, hosted by Georgia Tech), and
  • NYC, NY (CJ 2014, hosted by Columbia U).
  • Next one is being hosted by Stanford and will be in Palo Alto, CA.
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