Meta-classifiers for multimodal document classification

Scott Deeann Chen; Vishal Monga; Pierre Moulin

doi:10.1109/MMSP.2009.5293343

Meta-classifiers for multimodal document classification

Scott Deeann Chen, Vishal Monga, Pierre Moulin

Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Scopus citations

Abstract

This paper proposes learning algorithms for the problem of multimodal document classification. Specifically, we develop classifiers that automatically assign documents to categories by exploiting features from both text as well as image content. In particular, we use meta-classifiers that combine state-of-the-art text and image based classifiers into making joint decisions. The two meta classifiers we choose are based on support vector machines and Adaboost. Experiments on real-world databases from Wikipedia demonstrate the benefits of a joint exploitation of these modalities.

Original language	English (US)
Title of host publication	2009 IEEE International Workshop on Multimedia Signal Processing, MMSP '09
DOIs	https://doi.org/10.1109/MMSP.2009.5293343
State	Published - 2009
Event	2009 IEEE International Workshop on Multimedia Signal Processing, MMSP '09 - Rio De Janeiro, Brazil Duration: Oct 5 2009 → Oct 7 2009

Other

Other	2009 IEEE International Workshop on Multimedia Signal Processing, MMSP '09
Country/Territory	Brazil
City	Rio De Janeiro
Period	10/5/09 → 10/7/09

All Science Journal Classification (ASJC) codes

Artificial Intelligence
Computer Networks and Communications
Computer Vision and Pattern Recognition
Signal Processing

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10.1109/MMSP.2009.5293343

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title = "Meta-classifiers for multimodal document classification",

abstract = "This paper proposes learning algorithms for the problem of multimodal document classification. Specifically, we develop classifiers that automatically assign documents to categories by exploiting features from both text as well as image content. In particular, we use meta-classifiers that combine state-of-the-art text and image based classifiers into making joint decisions. The two meta classifiers we choose are based on support vector machines and Adaboost. Experiments on real-world databases from Wikipedia demonstrate the benefits of a joint exploitation of these modalities.",

author = "Chen, {Scott Deeann} and Vishal Monga and Pierre Moulin",

year = "2009",

doi = "10.1109/MMSP.2009.5293343",

language = "English (US)",

isbn = "9781424444649",

booktitle = "2009 IEEE International Workshop on Multimedia Signal Processing, MMSP '09",

note = "2009 IEEE International Workshop on Multimedia Signal Processing, MMSP '09 ; Conference date: 05-10-2009 Through 07-10-2009",

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T1 - Meta-classifiers for multimodal document classification

AU - Chen, Scott Deeann

AU - Monga, Vishal

AU - Moulin, Pierre

PY - 2009

Y1 - 2009

N2 - This paper proposes learning algorithms for the problem of multimodal document classification. Specifically, we develop classifiers that automatically assign documents to categories by exploiting features from both text as well as image content. In particular, we use meta-classifiers that combine state-of-the-art text and image based classifiers into making joint decisions. The two meta classifiers we choose are based on support vector machines and Adaboost. Experiments on real-world databases from Wikipedia demonstrate the benefits of a joint exploitation of these modalities.

AB - This paper proposes learning algorithms for the problem of multimodal document classification. Specifically, we develop classifiers that automatically assign documents to categories by exploiting features from both text as well as image content. In particular, we use meta-classifiers that combine state-of-the-art text and image based classifiers into making joint decisions. The two meta classifiers we choose are based on support vector machines and Adaboost. Experiments on real-world databases from Wikipedia demonstrate the benefits of a joint exploitation of these modalities.

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UR - http://www.scopus.com/inward/citedby.url?scp=74349088575&partnerID=8YFLogxK

U2 - 10.1109/MMSP.2009.5293343

DO - 10.1109/MMSP.2009.5293343

M3 - Conference contribution

AN - SCOPUS:74349088575

SN - 9781424444649

BT - 2009 IEEE International Workshop on Multimedia Signal Processing, MMSP '09

T2 - 2009 IEEE International Workshop on Multimedia Signal Processing, MMSP '09

Y2 - 5 October 2009 through 7 October 2009

ER -

Meta-classifiers for multimodal document classification

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