An Exploration of Multimodal Document Classification Strategies

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.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2011

Author dc:creator

• Chen, Scott D.

Contributors dc:contributor

• Moulin, Pierre

• meta-classifier
• classification
• multimodal
• document
• support vector machines

Statement dc:rights

• Copyright 2011 Scott Deeann Chen

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/24006

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/24006