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oai:www.ideals.illinois.edu:2142/24006 2023-07-10 col_2142_5131 col_2142_8888 com_2142_5130 com_2142_8887 com_2142_234

2011-05-25T14:51:49Z 2011-05 Moulin, Pierre Chen, Scott D. 2011-05-25T14:51:49Z 2011-05-25T14:51:49Z 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. Item withdrawn by Alexis Thompson (athmpsn1@illinois.edu) on 2011-04-21T21:58:29Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 2 Thesis.zip: 11862883 bytes, checksum: 46216d135279c32e59a6597580e78534 (MD5) Chen_Scott.pdf: 983244 bytes, checksum: a9d64c963139d763a78c2020cb845c5f (MD5) Made available in DSpace on 2011-05-25T14:51:49Z (GMT). No. of bitstreams: 3 Chen_Scott.pdf: 983243 bytes, checksum: ea4ccde7488ad18a7fc9395ceb0abd9e (MD5) license.txt: 4059 bytes, checksum: bd0ec53acac905061ab056f7df2ff7a6 (MD5) Thesis.zip: 11862883 bytes, checksum: 46216d135279c32e59a6597580e78534 (MD5) http://hdl.handle.net/2142/24006 en Copyright 2011 Scott Deeann Chen meta-classifier classification multimodal document support vector machines An Exploration of Multimodal Document Classification Strategies Electrical & Computer Eng 1933 Electrical & Computer Engr 1200 University of Illinois at Urbana-Champaign Thesis M.S. MS:Electr & Computer Eng-UIUC 10KS1200MS