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dc.contributor.authorLam, H.
dc.contributor.authorEkong, U.
dc.contributor.authorLiu, H.
dc.contributor.authorXiao, B.
dc.contributor.authorAraujo, H.
dc.contributor.authorLing, S.H.
dc.contributor.authorChan, Kit Yan
dc.identifier.citationLam, H. and Ekong, U. and Liu, H. and Xiao, B. and Araujo, H. and Ling, S.H. and Chan, K.Y. 2014. A study of neural-network-based classifiers for material classification. Neurocomputing. 144: pp. 367-377.

In this paper, the performance of the commonly used neural-network-based classifiers is investigated on solving a classification problem which aims to identify the object nature based on surface features of the object. When the surface data is obtained, a proposed feature extraction method is used to extract the surface feature of the object. The extracted features are then used as the inputs for the classifier. This research studies eighteen household objects which are requisite to our daily life. Six commonly used neural-network-based classifiers, namely one-against-all, weighted one-against-all, binary coded, parallel-structured, weighted parallel structured and tree-structured, are investigated. The performance for the six neural-network-based classifiers is evaluated based on recognition accuracy for individual object. Also, two traditional classifiers, namely k-nearest neighbor classifier and naïve Bayes classifier, are employed for comparison purposes. To evaluate robustness property of the classifiers, the original data is contaminated with Gaussian white noise. Experimental results show that the parallel-structured, tree-structured and the naïve Bayes classifiers outperform the others under the original data. The tree- structured classifier demonstrates the best robustness property under the noisy data.

dc.publisherElsevier BV
dc.subjectNeural Networks
dc.subjectMaterial Classification
dc.titleA study of neural-network-based classifiers for material classification
dc.typeJournal Article
curtin.departmentDepartment of Electrical and Computer Engineering
curtin.accessStatusFulltext not available

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