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Acoustic Vehicle Classification by Fusing with Semantic Annotation

Acoustic Vehicle Classification by Fusing with Semantic Annotation
Acoustic Vehicle Classification by Fusing with Semantic Annotation
Current research on acoustic vehicle classification has been generally aimed at utilizing various feature extraction methods and pattern recognition techniques. Previous research in gait biometrics has shown that domain knowledge or semantic enrichment can assist in improving the classification accuracy. In this paper, we address the problem of semantic enrichment by learning the semantic attributes from the training set, and then formalize the domain knowledge by using ontologies. We first consider a simple data ontology, and discuss how to use it for classification. Next we propose a scheme, which uses a semantic attribute to mediate information fusion for acoustic vehicle classification. To assess the proposed approaches, experiments are carried out based on a data set containing acoustic signals from five types of vehicles. Results indicate that whether the above semantic enrichment can lead to improvement depends on the accuracy of semantic annotation. Among the two enrichment schemes, semantically mediated information fusion achieves less significant improvement, but is insensitive to the annotation error.
Guo, Baofeng
e62b04c7-167b-45d9-a400-67a631861f24
Nixon, Mark
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Damarla, Raju
173e7e77-3e54-4bcb-999b-bca7b1e4a529
Guo, Baofeng
e62b04c7-167b-45d9-a400-67a631861f24
Nixon, Mark
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Damarla, Raju
173e7e77-3e54-4bcb-999b-bca7b1e4a529

Guo, Baofeng, Nixon, Mark and Damarla, Raju (2009) Acoustic Vehicle Classification by Fusing with Semantic Annotation. 12th IEEE International Conference on Information Fusion.

Record type: Conference or Workshop Item (Other)

Abstract

Current research on acoustic vehicle classification has been generally aimed at utilizing various feature extraction methods and pattern recognition techniques. Previous research in gait biometrics has shown that domain knowledge or semantic enrichment can assist in improving the classification accuracy. In this paper, we address the problem of semantic enrichment by learning the semantic attributes from the training set, and then formalize the domain knowledge by using ontologies. We first consider a simple data ontology, and discuss how to use it for classification. Next we propose a scheme, which uses a semantic attribute to mediate information fusion for acoustic vehicle classification. To assess the proposed approaches, experiments are carried out based on a data set containing acoustic signals from five types of vehicles. Results indicate that whether the above semantic enrichment can lead to improvement depends on the accuracy of semantic annotation. Among the two enrichment schemes, semantically mediated information fusion achieves less significant improvement, but is insensitive to the annotation error.

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More information

Published date: July 2009
Additional Information: Event Dates: July 2009
Venue - Dates: 12th IEEE International Conference on Information Fusion, 2009-07-01
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 267665
URI: https://eprints.soton.ac.uk/id/eprint/267665
PURE UUID: e3246540-e5e3-4dbc-9839-7c834db9ff9d
ORCID for Mark Nixon: ORCID iD orcid.org/0000-0002-9174-5934

Catalogue record

Date deposited: 14 Jul 2009 08:49
Last modified: 06 Jun 2018 13:18

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