Article

Evaluating the application of semantic inferencing rules to image annotation

Authors:

J. HunterAuthors Info & Claims

K-CAP '05: Proceedings of the 3rd international conference on Knowledge capture

Pages 91 - 98

https://doi.org/10.1145/1088622.1088639

Published: 02 October 2005 Publication History

Abstract

Semantic annotation of digital objects within large multimedia collections is a difficult and challenging task. We describe a method for semi-automatic annotation of images and apply it to and evaluate it on images of pancreatic cells. By comparing the performance of this approach in the pancreatic cell domain with previous results in the fuel cell domain, we aim to determine characteristics of a domain which indicate that the method will or will not work in that domain. We conclude by describing the types of images and domains in which we can expect satisfactory results with this approach.

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Cited By

Kim SJeon TRhiu IAhn JIm D(2021)Semantic Scene Graph Generation Using RDF Model and Deep LearningApplied Sciences10.3390/app1102082611:2(826)Online publication date: 17-Jan-2021
https://doi.org/10.3390/app11020826
Huang ZShi Z(2020)Image Caption Combined with GAN Training MethodIntelligent Information Processing X10.1007/978-3-030-46931-3_29(310-316)Online publication date: 26-Jun-2020
https://doi.org/10.1007/978-3-030-46931-3_29
Yang JMeng Z(2018)Image Semantic Description Based on Deep Learning with Multi-attention MechanismsIntelligent Information Processing IX10.1007/978-3-030-00828-4_36(356-362)Online publication date: 26-Sep-2018
https://doi.org/10.1007/978-3-030-00828-4_36
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Index Terms

Evaluating the application of semantic inferencing rules to image annotation
1. Information systems

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cover image ACM Conferences

K-CAP '05: Proceedings of the 3rd international conference on Knowledge capture

October 2005

234 pages

ISBN:1595931635

DOI:10.1145/1088622

General Chairs:
Peter Clark
The Boeing Company, USA
,
Guus Schreiber
Free University of Amsterdam, The Netherlands

Copyright © 2005 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 02 October 2005

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K-Cap05

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K-Cap05: Third International Conference on Knowledge Capture 2005

October 2 - 5, 2005

Alberta, Banff, Canada

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Overall Acceptance Rate 55 of 198 submissions, 28%

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Cited By

Kim SJeon TRhiu IAhn JIm D(2021)Semantic Scene Graph Generation Using RDF Model and Deep LearningApplied Sciences10.3390/app1102082611:2(826)Online publication date: 17-Jan-2021
https://doi.org/10.3390/app11020826
Huang ZShi Z(2020)Image Caption Combined with GAN Training MethodIntelligent Information Processing X10.1007/978-3-030-46931-3_29(310-316)Online publication date: 26-Jun-2020
https://doi.org/10.1007/978-3-030-46931-3_29
Yang JMeng Z(2018)Image Semantic Description Based on Deep Learning with Multi-attention MechanismsIntelligent Information Processing IX10.1007/978-3-030-00828-4_36(356-362)Online publication date: 26-Sep-2018
https://doi.org/10.1007/978-3-030-00828-4_36
Alves MDamásio CCorreia Nde Jesus Lima Gomes Fde Andrade Lira Rabelo Rde Salles Soares Neto CWillrich RCamillo Teixeira Cde Almeida Jde Carvalho W(2016)A Semantic Web Approach to Low-Level Features in ImagesProceedings of the 22nd Brazilian Symposium on Multimedia and the Web10.1145/2976796.2988176(195-198)Online publication date: 8-Nov-2016
https://dl.acm.org/doi/10.1145/2976796.2988176
Yan ZGe HLiu Z(2014)The Study on Face Contour Description Based on ASM and Semantic TechniquesFuture Information Technology10.1007/978-3-642-55038-6_59(385-390)Online publication date: 2014
https://doi.org/10.1007/978-3-642-55038-6_59
Wang ZGuan GQiu YZhuo LFeng D(2013)Semantic context based refinement for news video annotationMultimedia Tools and Applications10.1007/s11042-012-1060-x67:3(607-627)Online publication date: 1-Dec-2013
https://dl.acm.org/doi/10.1007/s11042-012-1060-x
Wang ZFeng D(2012)Discovering Semantics from Visual InformationMachine Learning10.4018/978-1-60960-818-7.ch808(1981-2009)Online publication date: 2012
https://doi.org/10.4018/978-1-60960-818-7.ch808
Saad SMahmoudi SManneback PPresutti VPinto H(2012)Semantic analysis of human movements in videosProceedings of the 8th International Conference on Semantic Systems10.1145/2362499.2362519(141-148)Online publication date: 5-Sep-2012
https://dl.acm.org/doi/10.1145/2362499.2362519
aad SDe Beul DMahmoudi SManneback P(2012)An Ontology for video human movement representation based on Benesh notation2012 International Conference on Multimedia Computing and Systems10.1109/ICMCS.2012.6320129(77-82)Online publication date: May-2012
https://doi.org/10.1109/ICMCS.2012.6320129
Dalakleidi KDasiopoulou SStoilos GTzouvaras VStamou GKompatsiaris Y(2011)Semantic representation of multimedia contentKnowledge-driven multimedia information extraction and ontology evolution10.5555/2001069.2001071(18-49)Online publication date: 1-Jan-2011
https://dl.acm.org/doi/10.5555/2001069.2001071
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