poster

Multimedia event recounting with concept based representation

Authors:

Qian Yu,

Jingen Liu,

Hui Cheng,

Ajay Divakaran,

Harpreet SawhneyAuthors Info & Claims

MM '12: Proceedings of the 20th ACM international conference on Multimedia

Pages 1073 - 1076

https://doi.org/10.1145/2393347.2396386

Published: 29 October 2012 Publication History

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Abstract

Multimedia event detection has drawn a lot of attention in recent years. Given a recognized event, in this paper, we conduct a pilot study of the multimedia event recounting problem, which answers the question why this video is recognized as this event, i.e. what evidences this decision is made on. In order to provide a semantic recounting of the multimedia event, we adopt a concept-based event representation for learning a discriminative event model. Then, we present a recounting approach that exactly recovers the contribution of semantic evidence to the event classification decision. This approach can be applied on any additive discriminative classifiers. The promising result is shown on the MED11 dataset that contains 15 events in thousands of YouTube like videos.

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Cited By

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Marcos DLobry STuia D(2019)Semantically Interpretable Activation Maps: what-where-how explanations within CNNs2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)10.1109/ICCVW.2019.00518(4207-4215)Online publication date: Oct-2019
https://doi.org/10.1109/ICCVW.2019.00518
Wu TSong X(2019)Towards Interpretable Object Detection by Unfolding Latent Structures2019 IEEE/CVF International Conference on Computer Vision (ICCV)10.1109/ICCV.2019.00613(6032-6042)Online publication date: Oct-2019
https://doi.org/10.1109/ICCV.2019.00613
Mulvey DFoh CImran MTafazolli R(2019)Cell Fault Management Using Machine Learning TechniquesIEEE Access10.1109/ACCESS.2019.29384107(124514-124539)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2938410
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Index Terms

Multimedia event recounting with concept based representation
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
      2. Computer vision tasks
        Scene understanding
        Video summarization

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Information & Contributors

Information

Published In

MM '12: Proceedings of the 20th ACM international conference on Multimedia

October 2012

1584 pages

ISBN:9781450310895

DOI:10.1145/2393347

General Chairs:
Noboru Babaguchi
Osaka University, Japan
,
Kiyoharu Aizawa
The University of Tokyo, Japan
,
John Smith
IBM, USA
,
Program Chairs:
Shin'ichi Satoh
National Institute of Informatics, Japan
,
Thomas Plagemann
University of Oslo, Norway
,
Xian-Sheng Hua
Microsoft, USA
,
Rong Yan
Facebook, USA

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 October 2012

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MM '12

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SIGMM

MM '12: ACM Multimedia Conference

October 29 - November 2, 2012

Nara, Japan

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Overall Acceptance Rate 1,548 of 5,826 submissions, 27%

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Cited By

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Marcos DLobry STuia D(2019)Semantically Interpretable Activation Maps: what-where-how explanations within CNNs2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)10.1109/ICCVW.2019.00518(4207-4215)Online publication date: Oct-2019
https://doi.org/10.1109/ICCVW.2019.00518
Wu TSong X(2019)Towards Interpretable Object Detection by Unfolding Latent Structures2019 IEEE/CVF International Conference on Computer Vision (ICCV)10.1109/ICCV.2019.00613(6032-6042)Online publication date: Oct-2019
https://doi.org/10.1109/ICCV.2019.00613
Mulvey DFoh CImran MTafazolli R(2019)Cell Fault Management Using Machine Learning TechniquesIEEE Access10.1109/ACCESS.2019.29384107(124514-124539)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2938410
Keneni BKaur DAl Bataineh ADevabhaktuni VJavaid AZaientz JMarinier R(2019)Evolving Rule-Based Explainable Artificial Intelligence for Unmanned Aerial VehiclesIEEE Access10.1109/ACCESS.2019.28931417(17001-17016)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2893141
Inoue NShinoda KBoll SMu Lee KLuo JZhu WByun HWen Chen CLienhart RMei T(2018)Few-Shot Adaptation for Multimedia Semantic IndexingProceedings of the 26th ACM international conference on Multimedia10.1145/3240508.3240592(1110-1118)Online publication date: 15-Oct-2018
https://dl.acm.org/doi/10.1145/3240508.3240592
Min WBao BMei SZhu YRui YJiang S(2018)You Are What You EatIEEE Transactions on Multimedia10.1109/TMM.2017.275949920:4(950-964)Online publication date: 1-Apr-2018
https://dl.acm.org/doi/10.1109/TMM.2017.2759499
Han XSingh BMorariu VDavis L(2017)VRFP: On-the-Fly Video Retrieval Using Web Images and Fast Fisher Vector ProductsIEEE Transactions on Multimedia10.1109/TMM.2017.267141419:7(1583-1595)Online publication date: 1-Jul-2017
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Escalante HGuyon IEscalera SJacques JMadadi MBaro XAyache SViegas EGucluturk YGuclu Uvan Gerven Mvan Lier R(2017)Design of an explainable machine learning challenge for video interviews2017 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN.2017.7966320(3688-3695)Online publication date: May-2017
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Hinami RMei TSatoh S(2017)Joint Detection and Recounting of Abnormal Events by Learning Deep Generic Knowledge2017 IEEE International Conference on Computer Vision (ICCV)10.1109/ICCV.2017.391(3639-3647)Online publication date: Oct-2017
https://doi.org/10.1109/ICCV.2017.391
Li WVasconcelos N(2017)Complex Activity Recognition Via Attribute DynamicsInternational Journal of Computer Vision10.1007/s11263-016-0918-1122:2(334-370)Online publication date: 1-Apr-2017
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