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News Event Understanding by Mining Latent Factors From Multimodal Tensors

Published:16 October 2016Publication History

ABSTRACT

We present a novel and efficient constrained tensor factorization algorithm that first represents a video archive, of multimedia news stories concerning a news event, as a sparse tensor of order 4. The dimensions correspond to extracted visual memes, verbal tags, time periods and cultures. The iterative algorithm then approximately but accurately ex- tracts coherent quad-clusters, each of which represents a significant summary of an important independent aspect of the news event. We give examples of quad-clusters extracted from tensors with at least 108 entries derived from the international news coverage of the Ebola epidemic, AirAsia flight Q8501 and Zika virus. We show the method is fast, can be tuned to give preferences to any subset of its four dimensions, and exceeds three existing methods in performance.

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            cover image ACM Conferences
            iV&L-MM '16: Proceedings of the 2016 ACM workshop on Vision and Language Integration Meets Multimedia Fusion
            October 2016
            70 pages
            ISBN:9781450345194
            DOI:10.1145/2983563

            Copyright © 2016 ACM

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            • Published: 16 October 2016

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            iV&L-MM '16 Paper Acceptance Rate7of15submissions,47%Overall Acceptance Rate7of15submissions,47%

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