Context modeling with Bayesian network ensemble for recognizing objects in uncertain environments

Seung Bin Im, Youn Suk Song, Sung Bae Cho

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

It is difficult to understand a scene from visual information in uncertain real world. Since Bayesian network (BN) is known as good in this uncertainty, it has received significant attention in the area of vision-based scene understanding. However, BN-based modeling methods still have the difficulties in modeling complex relationships and combining several modules, as well as the high computational complexity of inference. To overcome them, this paper proposes a method to divide and select the BN modules for recognizing the objects in uncertain environments. The method utilizes the behavior selection network to select the most appropriate BN modules. Several experiments are performed to verify the usefulness of the proposed method.

Original languageEnglish
Title of host publicationFuzzy Systems and Knowledge Discovery - Third International Conference, FSKD 2006, Proceedings
PublisherSpringer Verlag
Pages688-691
Number of pages4
ISBN (Print)3540459162, 9783540459163
Publication statusPublished - 2006 Jan 1
Event3rd International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2006 - Xi'an, China
Duration: 2006 Sep 242006 Sep 28

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4223 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other3rd International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2006
CountryChina
CityXi'an
Period06/9/2406/9/28

Fingerprint

Context Modeling
Bayesian networks
Bayesian Networks
Ensemble
Module
Modeling Method
Divides
Computational complexity
Computational Complexity
Verify
Uncertainty
Object
Modeling
Experiment
Experiments
Vision

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Im, S. B., Song, Y. S., & Cho, S. B. (2006). Context modeling with Bayesian network ensemble for recognizing objects in uncertain environments. In Fuzzy Systems and Knowledge Discovery - Third International Conference, FSKD 2006, Proceedings (pp. 688-691). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4223 LNAI). Springer Verlag.
Im, Seung Bin ; Song, Youn Suk ; Cho, Sung Bae. / Context modeling with Bayesian network ensemble for recognizing objects in uncertain environments. Fuzzy Systems and Knowledge Discovery - Third International Conference, FSKD 2006, Proceedings. Springer Verlag, 2006. pp. 688-691 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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abstract = "It is difficult to understand a scene from visual information in uncertain real world. Since Bayesian network (BN) is known as good in this uncertainty, it has received significant attention in the area of vision-based scene understanding. However, BN-based modeling methods still have the difficulties in modeling complex relationships and combining several modules, as well as the high computational complexity of inference. To overcome them, this paper proposes a method to divide and select the BN modules for recognizing the objects in uncertain environments. The method utilizes the behavior selection network to select the most appropriate BN modules. Several experiments are performed to verify the usefulness of the proposed method.",
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Im, SB, Song, YS & Cho, SB 2006, Context modeling with Bayesian network ensemble for recognizing objects in uncertain environments. in Fuzzy Systems and Knowledge Discovery - Third International Conference, FSKD 2006, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4223 LNAI, Springer Verlag, pp. 688-691, 3rd International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2006, Xi'an, China, 06/9/24.

Context modeling with Bayesian network ensemble for recognizing objects in uncertain environments. / Im, Seung Bin; Song, Youn Suk; Cho, Sung Bae.

Fuzzy Systems and Knowledge Discovery - Third International Conference, FSKD 2006, Proceedings. Springer Verlag, 2006. p. 688-691 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4223 LNAI).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AB - It is difficult to understand a scene from visual information in uncertain real world. Since Bayesian network (BN) is known as good in this uncertainty, it has received significant attention in the area of vision-based scene understanding. However, BN-based modeling methods still have the difficulties in modeling complex relationships and combining several modules, as well as the high computational complexity of inference. To overcome them, this paper proposes a method to divide and select the BN modules for recognizing the objects in uncertain environments. The method utilizes the behavior selection network to select the most appropriate BN modules. Several experiments are performed to verify the usefulness of the proposed method.

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Im SB, Song YS, Cho SB. Context modeling with Bayesian network ensemble for recognizing objects in uncertain environments. In Fuzzy Systems and Knowledge Discovery - Third International Conference, FSKD 2006, Proceedings. Springer Verlag. 2006. p. 688-691. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).