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    Semi-supervised tensor-based graph embedding learning and its application to visual discriminant tracking

    Hu, W. and Gao, J. and Xing, J. and Zhang, C. and Maybank, Stephen (2017) Semi-supervised tensor-based graph embedding learning and its application to visual discriminant tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 39 (1), pp. 172-188. ISSN 0162-8828.

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    An appearance model adaptable to changes in object appearance is critical in visual object tracking. In this paper, we treat an image patch as a 2-order tensor which preserves the original image structure. We design two graphs for characterizing the intrinsic local geometrical structure of the tensor samples of the object and the background. Graph embedding is used to reduce the dimensions of the tensors while preserving the structure of the graphs. Then, a discriminant embedding space is constructed. We prove two propositions for finding the transformation matrices which are used to map the original tensor samples to the tensor-based graph embedding space. In order to encode more discriminant information in the embedding space, we propose a transfer-learningbased semi-supervised strategy to iteratively adjust the embedding space into which discriminative information obtained from earlier times is transferred. We apply the proposed semi-supervised tensor-based graph embedding learning algorithm to visual tracking. The new tracking algorithm captures an object’s appearance characteristics during tracking and uses a particle filter to estimate the optimal object state. Experimental results on the CVPR 2013 benchmark dataset demonstrate the effectiveness of the proposed tracking algorithm.


    Item Type: Article
    Additional Information: (c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
    Keyword(s) / Subject(s): Discriminant tracking, Tensor samples, Semi-supervised learning, Graph embedding space
    School: School of Business, Economics & Informatics > Computer Science and Information Systems
    Depositing User: Administrator
    Date Deposited: 19 Apr 2016 11:15
    Last Modified: 29 Jun 2020 17:23


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