An Exploration of Multimodal Document Classification Strategies
Chen, Scott D.
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https://hdl.handle.net/2142/24006
Description
Title
An Exploration of Multimodal Document Classification Strategies
Author(s)
Chen, Scott D.
Issue Date
2011-05-25T14:51:49Z
Director of Research (if dissertation) or Advisor (if thesis)
Moulin, Pierre
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
meta-classifier
classification
multimodal
document
support vector machines
Abstract
This thesis explores multimodal document classification algorithms in a unified framework. Classification algorithms are designed to exploit both text and image information, which proliferates in modern documents. We design meta-classification schemes that combine and integrate state-of-the-art text and image feature-extractors with state-of-the-art classifiers. Meta-classifiers fuse information across modalities that differ in nature and hence have more information on hand to make decisions. This thesis also discusses strategies that exploit correlations not only within a single modality but also among modalities. Techniques that exploit correlations within a modality include image meta-feature vector combination and latent Dirichlet allocation-based image meta-feature extraction. Another technique that exploits correlations between text and image cleans image with text information. Experiments on real-world databases from Wikipedia demonstrate the benefits of metaclassification for multimodal documents.
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