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: ACM Multimedia (2008) “Audio Puzzler: Piecing Together Time-Stamped Speech Transcripts with a Puzzle Game”

October 18th, 2008 Irfan Essa Posted in ACM MM, Computational Journalism, Multimedia, Nick Diakopoulos, Papers No Comments »

N. Diakopoulos, K. Luther, I. Essa (2008), “Audio Puzzler: Piecing Together Time-Stamped Speech Transcripts with a Puzzle Game.” In Proceedings of  ACM International Conference on Multimedia 2008. Vancouver, BC, CANANDA  [Project Link]

ABSTRACT

We have developed an audio-based casual puzzle game which produces a time-stamped transcription of spokenapaudio as a by-product of play. Our evaluation of the game indicates that it is both fun and challenging. The transcripts generated using the game are more accurate than those produced using a standard automatic transcription system and the time-stamps of words are within several hundred milliseconds of ground truth.

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Paper in ACM Multimedia (2006): “Interactive mosaic generation for video navigation”

October 22nd, 2006 Irfan Essa Posted in ACM MM, Computational Photography and Video, Gregory Abowd, Kihwan Kim, Multimedia, Papers No Comments »

K. Kim, I. Essa, and G. Abowd (2006) “Interactive mosaic generation for video navigation.” in Proceedings of the 14th annual ACM international conference on Multimedia, pages 655-658, 2006. [Project Page | DOI | PDF]

Abstract

Navigation through large multimedia collections that include videos and images still remains cumbersome. In this paper, we introduce a novel method to visualize and navigate through the collection by creating a mosaic image that visually represents the compilation. This image is generated by a labeling-based layout algorithm using various sizes of sample tile images from the collection. Each tile represents both the photographs and video files representing scenes selected by matching algorithms. This generated mosaic image provides a new way for thematic video and visually summarizes the videos. Users can generate these mosaics with some predefined themes and layouts, or base it on the results of their queries. Our approach supports automatic generation of these layouts by using meta-information such as color, time-line and existence of faces or manually generated annotated information from existing systems (e.g., the Family Video Archive).

Interactive Video Mosaic

Interactive Video Mosaic

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