Please use this identifier to cite or link to this item: https://doi.org/10.1109/TKDE.2004.1269600
DC FieldValue
dc.titleAddressing the Problems of Bayesian Network Classification of Video Using High-Dimensional Features
dc.contributor.authorMittal, A.
dc.contributor.authorCheong, L.-F.
dc.date.accessioned2013-07-23T09:32:19Z
dc.date.available2013-07-23T09:32:19Z
dc.date.issued2004
dc.identifier.citationMittal, A., Cheong, L.-F. (2004). Addressing the Problems of Bayesian Network Classification of Video Using High-Dimensional Features. IEEE Transactions on Knowledge and Data Engineering 16 (2) : 230-244. ScholarBank@NUS Repository. https://doi.org/10.1109/TKDE.2004.1269600
dc.identifier.issn10414347
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/43373
dc.description.abstractBayesian theory is of great interest in pattern classification. In this paper, we present an approach to aid in the effective application of Bayesian networks in tasks like video classification, where descriptors originate from varied sources and are large in number. In order to extend the application of conventional Bayesian theory to the case of continuous and nonparametric descriptor space, dimension partitioning into attributes by minimizing the discrete Bayes error is proposed. The partitioning output goes to the dimensionality reduction module. A new algorithm for dimensionality reduction for improving the classification accuracy is proposed based on the class pair discriminative capacity of the dimensions. It is also shown how attributes can be weighed automatically in a single-label assignment based on comparing the class pairs. A computationally efficient method to assign multiple labels on the samples is also presented. Comparison with standard classification tools on video data of more than 4,000 segments shows the potential of our approach in pattern classification.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/TKDE.2004.1269600
dc.sourceScopus
dc.subjectBayesian networks
dc.subjectContent-based retrieval
dc.subjectDimensionality reduction
dc.subjectDiscrete bayes error
dc.subjectMultiple labels assignment
dc.subjectPartitioning
dc.typeReview
dc.contributor.departmentCOMPUTER SCIENCE
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.doi10.1109/TKDE.2004.1269600
dc.description.sourcetitleIEEE Transactions on Knowledge and Data Engineering
dc.description.volume16
dc.description.issue2
dc.description.page230-244
dc.description.codenITKEE
dc.identifier.isiut000188294300007
Appears in Collections:Staff Publications

Show simple item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

23
checked on Jan 16, 2020

WEB OF SCIENCETM
Citations

15
checked on Jan 16, 2020

Page view(s)

147
checked on Dec 30, 2019

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.