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  1. Transactions on Multimedia Computing, Communications, and Applications (TOMM)
  2. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) : Volume 11
  3. Issue 1, August 2014
  4. Up-Fusion: An Evolving Multimedia Fusion Method
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ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) : Volume 13
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) : Volume 12
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) : Volume 11
Issue 4, April 2015
Issue 2s(Special Issue on MMSYS 2014), February 2015
Issue 3, January 2015
Issue 2, December 2014
Issue 1s(Special Issue on Multiple Sensorial (MulSeMedia) Multimodal Media : Advances and Applications), September 2014
Issue 1, August 2014
Editorial Note
A Sketch-Based Approach for Interactive Organization of Video Clips
Circle & Search: Attribute-Aware Shoe Retrieval
A Top-Down Approach for Video Summarization
PROPANE: A Progressive Panorama Streaming Protocol to Support Interactive 3D Virtual Environment Exploration on Graphics-Constrained Devices
Up-Fusion: An Evolving Multimedia Fusion Method
Exploration in Interactive Personalized Music Recommendation: A Reinforcement Learning Approach
Online Estimation of Evolving Human Visual Interest
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) : Volume 10
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) : Volume 9
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) : Volume 8

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Up-Fusion: An Evolving Multimedia Fusion Method

Content Provider ACM Digital Library
Author Wang, Xiangyu Kankanhalli, Mohan Rui, Yong
Copyright Year 2014
Abstract The amount of multimedia data on the Internet has increased exponentially in the past few decades and this trend is likely to continue. Multimedia content inherently has multiple information sources, therefore effective fusion methods are critical for data analysis and understanding. So far, most of the existing fusion methods are static with respect to time, making it difficult for them to handle the evolving multimedia content. To address this issue, in recent years, several evolving fusion methods were proposed, however, their requirements are difficult to meet, making them useful only in limited applications. In this article, we propose a novel evolving fusion method based on the online portfolio selection theory. The proposed method takes into account the correlation among different information sources and evolves the fusion model when new multimedia data is added. It performs effectively on both crisp and soft decisions without requiring additional context information. Extensive experiments on concept detection and human detection tasks over the TRECVID dataset and surveillance data have been conducted and significantly better performance has been obtained.
Starting Page 1
Ending Page 19
Page Count 19
File Format PDF
ISSN 15516857
e-ISSN 15516865
DOI 10.1145/2611777
Volume Number 11
Issue Number 1
Journal ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM)
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2014-09-04
Publisher Place New York
Access Restriction One Nation One Subscription (ONOS)
Subject Keyword Up-fusion Fusion Portfolio theory
Content Type Text
Resource Type Article
Subject Hardware and Architecture Computer Networks and Communications
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