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Distributed multimedia content analysis with MapReduce | IEEE Conference Publication | IEEE Xplore

Distributed multimedia content analysis with MapReduce


Abstract:

This paper introduces a scalable solution for distributing content-based video analysis tasks using the emerging MapReduce programming model. Scalable and efficient solut...Show More

Abstract:

This paper introduces a scalable solution for distributing content-based video analysis tasks using the emerging MapReduce programming model. Scalable and efficient solutions are needed for this type of tasks, as the number of multimedia content is growing at an increasing rate. We present a novel implementation utilizing the popular Apache Hadoop MapReduce framework for both analysis job scheduling and video data distribution. We employ face detection as a case example because it represents a popular visual content analysis task. The main contribution of this paper is the performance evaluation of distribution models for video content processing in various configurations. In our experiments, we have compared the performance of our video data distribution method against two alternatives solutions on a seven node cluster. Hadoop's performance overhead in video content analysis was also evaluated. We found Hadoop to be a data efficient solution with minimal computational overhead for the face detection task.
Date of Conference: 08-11 September 2013
Date Added to IEEE Xplore: 25 November 2013
Electronic ISBN:978-1-4673-6235-1

ISSN Information:

Conference Location: London, UK

References

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