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    A study of neural-network-based classifiers for material classification

    Access Status
    Fulltext not available
    Authors
    Lam, H.
    Ekong, U.
    Liu, H.
    Xiao, B.
    Araujo, H.
    Ling, S.H.
    Chan, Kit Yan
    Date
    2014
    Type
    Journal Article
    
    Metadata
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    Citation
    Lam, 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.
    Source Title
    Neurocomputing
    DOI
    10.1016/j.neucom.2014.05.019
    ISSN
    0925-2312
    School
    Department of Electrical and Computer Engineering
    URI
    http://hdl.handle.net/20.500.11937/5196
    Collection
    • Curtin Research Publications
    Abstract

    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.

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