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oai:www.ideals.illinois.edu:2142/46848 2023-07-11 col_2142_5131 col_2142_8888 com_2142_5130 com_2142_8887 com_2142_234

Huang, Thomas S. Chang, Shiyu 2014-01-16T18:18:31Z 2014-01-16T18:18:31Z 2016-01-16T11:01:37Z 2013-12 2014-01-16T18:18:31Z 2013-12 "Recent advances in multimedia research have generated a large collection of concept models, e.g., LSCOM and Mediamill 101, which have become accessible to other researchers. While most current research efforts still focus on building new concepts from scratch, little effort has been made to construct new concepts upon the existing models already in the ""warehouse"". To address this issue, we have developed a new framework in this thesis, termed LEarning structured model by probabilistic loGic Ontology (LEGO) to seamlessly integrate both the new target training examples and the existing primitive concept models. LEGO treats the primitive concept models as a Lego toy to potentially construct an unlimited vocabulary of new concepts. Specifically, LEGO first formulates the logic operations to be the Lego connectors used to combine existing concept models hierarchically in probabilistic logic ontology trees. LEGO then simultaneously incorporates new target training information to efficiently disambiguate the underlying logic tree and correct the error propagation. We present extensive experimental results on a large vehicle domain data set from ImageNet and demonstrate significantly superior performance over existing state-of-the-art approaches which build new concept models from scratch." Item withdrawn by Laura Spradlin (lspradl2@illinois.edu) on 2013-12-10T15:43:22Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 1 Chang_Shiyu.pdf: 1241664 bytes, checksum: a5186e9892a10ffcbcf26d05d2b3390a (MD5) Made available in DSpace on 2014-01-16T18:18:31Z (GMT). No. of bitstreams: 2 Shiyu_Chang.pdf: 1241664 bytes, checksum: a5186e9892a10ffcbcf26d05d2b3390a (MD5) license.txt: 4060 bytes, checksum: 640b1296a80104e0ab489b3fa7b03a1e (MD5) Item marked as restricted to the 'UIUC Users [automated]' Group (id=2) by Seth Robbins (robbins.sd@gmail.com) on 2014-01-16T18:19:48Z Item is restricted until 2016-01-16T18:19:34Z Restriction data tranferred 2014-07-01T11:20:28-05:00 Original Data Group with Access UIUC Users [automated] Release Date: 2016-01-16 12:19:34 UTC Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system U of I Only Restriction Lifted for Item 46867 on 2016-01-16T11:01:37Z. http://hdl.handle.net/2142/46848 en Copyright 2013 Shiyu Chang Multimedia LEarning structured model by probabilistic loGic Ontology (LEGO) Concept recycling Model warehouse Probabilistic logic ontology tree Logical operations Structured concept recycling by probabilistic logic ontology tree text Electrical & Computer Eng 1933 Electrical & Computer Engr 1200 University of Illinois at Urbana-Champaign Thesis M.S. MS:Electr & Computer Eng-UIUC 10KS1200MS