{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/46848"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/46848","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Structured concept recycling by probabilistic logic ontology tree","abstract":"\"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.\"","abstract_html":""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."","abstract_has_math":false,"creators":["Chang, Shiyu"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Huang, Thomas S."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2014,"date_issued":"2014-01-16T18:18:31Z","date_published":"2014-01-16T18:18:31Z","updated_at":"2026-07-22T22:25:38Z","subjects":["Multimedia LEarning structured model by probabilistic loGic Ontology (LEGO)","Concept recycling","Model warehouse","Probabilistic logic ontology tree","Logical operations"],"languages":["en"],"rights":["Copyright 2013 Shiyu Chang"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/46848","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Huang, Thomas S."]},{"key":"dc:creator","label":"Author","values":["Chang, Shiyu"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2014-01-16T18:18:31Z","2016-01-16T11:01:37Z","2013-12"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Multimedia LEarning structured model by probabilistic loGic Ontology (LEGO)","Concept recycling","Model warehouse","Probabilistic logic ontology tree","Logical operations"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2013 Shiyu Chang"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/46848"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["\"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. 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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). 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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). 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