Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/40454
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dc.titleAuto-annotation of paintings using social annotations, domain ontology and transductive inference
dc.contributor.authorLeslie, L.M.
dc.contributor.authorChua, T.-S.
dc.contributor.authorJain, R.
dc.date.accessioned2013-07-04T08:04:40Z
dc.date.available2013-07-04T08:04:40Z
dc.date.issued2007
dc.identifier.citationLeslie, L.M.,Chua, T.-S.,Jain, R. (2007). Auto-annotation of paintings using social annotations, domain ontology and transductive inference. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 4810 LNCS : 266-275. ScholarBank@NUS Repository.
dc.identifier.isbn9783540772545
dc.identifier.issn03029743
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/40454
dc.description.abstractKnowledge of paintings domain includes a variety of sources such as essays, visual examples, ontologies of artistic concepts and user- provided annotations. This knowledge serves several purposes. First, it defines a wide range of concepts for annotation and flexible retrieval of paintings. Second, it serves to bootstrap auto-annotation and disambiguate the generated candidate labels. Third, the user-provided annotations serve to discover folksonomies of concepts and vernacular terms. In this paper, we propose a framework for paintings auto-annotation that incorporates user provided images and annotations, domain ontology and external knowledge sources. We utilize these sources of information to bootstrap and support the auto-annotation task, which is based on transductive inference mechanism that combines probabilistic clustering and multi-expert approach to generate labels. We further combine user-provided annotations with generated labels and domain ontology to disambiguate the concepts. In our experiments, we focus on the auto-annotation of painting and demonstrate that the user-provided annotations significantly increase annotation accuracy. © Springer-Verlag Berlin Heidelberg 2007 Transductive Inference, Multi-expert Approach, Probabilistic Clustering,.
dc.sourceScopus
dc.subjectAnnotation
dc.subjectConcept disambiguation
dc.subjectDomain ontology
dc.subjectLearning
dc.subjectPaintings
dc.subjectSocial network
dc.typeConference Paper
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.sourcetitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.description.volume4810 LNCS
dc.description.page266-275
dc.identifier.isiutNOT_IN_WOS
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