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Cross-Domain Multi-Event Tracking via CO-PMHT

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Published:04 July 2014Publication History
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Abstract

With the massive growth of events on the Internet, efficient organization and monitoring of events becomes a practical challenge. To deal with this problem, we propose a novel CO-PMHT (CO-Probabilistic Multi-Hypothesis Tracking) algorithm for cross-domain multi-event tracking to obtain their informative summary details and evolutionary trends over time. We collect a large-scale dataset by searching keywords on two domains (Gooogle News and Flickr) and downloading both images and textual content for an event. Given the input data, our algorithm can track multiple events in the two domains collaboratively and boost the tracking performance. Specifically, the bridge between two domains is a semantic posterior probability, that avoids the domain gap. After tracking, we can visualize the whole evolutionary process of the event over time and mine the semantic topics of each event for deep understanding and event prediction. The extensive experimental evaluations on the collected dataset well demonstrate the effectiveness of the proposed algorithm for cross-domain multi-event tracking.

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          • Published in

            cover image ACM Transactions on Multimedia Computing, Communications, and Applications
            ACM Transactions on Multimedia Computing, Communications, and Applications  Volume 10, Issue 4
            June 2014
            132 pages
            ISSN:1551-6857
            EISSN:1551-6865
            DOI:10.1145/2656131
            Issue’s Table of Contents

            Copyright © 2014 ACM

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            Association for Computing Machinery

            New York, NY, United States

            Publication History

            • Published: 4 July 2014
            • Revised: 1 March 2014
            • Accepted: 1 March 2014
            • Received: 1 July 2013
            Published in tomm Volume 10, Issue 4

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