An Exploration of Multimodal Document Classification Strategies

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

Chen, Scott D. ; Moulin ; Pierre

关键词: meta-classifier;    classification;    multimodal;    document;    support vector machines;   

Others  :  https://www.ideals.illinois.edu/bitstream/handle/2142/24006/Chen_Scott.pdf?sequence=1&isAllowed=y

美国|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

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.