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  • Describing and learning of related parts based on latent structural model in big data

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    Author(s)
    Liu, Lei
    Bai, Xiao
    Zhang, Huigang
    Zhou, Jun
    Tang, Wenzhong
    Griffith University Author(s)
    Zhou, Jun
    Year published
    2016
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    Abstract
    In this paper, we propose a novel latent structural model for big data image recognition. It addresses the problem that large amount of labeled training samples are needed in traditional structural models. This method first builds an initial structural model by using only one labeled image. After pooling unlabeled samples into the initial model, an incremental learning process is used to find more candidate parts and to update the model. The appearance features of the parts are described by multiple kernel learning method that assembles more information of the parts, such as color, edge, and texture. Therefore, the proposed ...
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    In this paper, we propose a novel latent structural model for big data image recognition. It addresses the problem that large amount of labeled training samples are needed in traditional structural models. This method first builds an initial structural model by using only one labeled image. After pooling unlabeled samples into the initial model, an incremental learning process is used to find more candidate parts and to update the model. The appearance features of the parts are described by multiple kernel learning method that assembles more information of the parts, such as color, edge, and texture. Therefore, the proposed model considers not only independent components but also their inherent spatial and appearance relationships. Finally, the updated model is applied to recognition tasks. Experiments show that this method is effective in handling big data problems and has achieved better performance than several state-of-the-art methods.
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    Journal Title
    Neurocomputing
    Volume
    173
    Issue
    Part 2
    DOI
    https://doi.org/10.1016/j.neucom.2014.12.120
    Copyright Statement
    © 2016 Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited.
    Subject
    Engineering
    Psychology
    Publication URI
    http://hdl.handle.net/10072/142474
    Collection
    • Journal articles

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