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    Manifold regularized multitask learning for semi-supervised multilabel image classification

    Luo, Y. and Tao, D. and Geng, B. and Xu, C. and Maybank, Stephen J. (2013) Manifold regularized multitask learning for semi-supervised multilabel image classification. IEEE Transactions on Image Processing 22 (2), pp. 523-536. ISSN 1057-7149.

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    Abstract

    It is a significant challenge to classify images with multiple labels by using only a small number of labeled samples. One option is to learn a binary classifier for each label and use manifold regularization to improve the classification performance by exploring the underlying geometric structure of the data distribution. However, such an approach does not perform well in practice when images from multiple concepts are represented by high-dimensional visual features. Thus, manifold regularization is insufficient to control the model complexity. In this paper, we propose a manifold regularized multitask learning (MRMTL) algorithm. MRMTL learns a discriminative subspace shared by multiple classification tasks by exploiting the common structure of these tasks. It effectively controls the model complexity because different tasks limit one another's search volume, and the manifold regularization ensures that the functions in the shared hypothesis space are smooth along the data manifold. We conduct extensive experiments, on the PASCAL VOC'07 dataset with 20 classes and the MIR dataset with 38 classes, by comparing MRMTL with popular image classification algorithms. The results suggest that MRMTL is effective for image classification.

    Metadata

    Item Type: Article
    School: School of Business, Economics & Informatics > Computer Science and Information Systems
    Depositing User: Sarah Hall
    Date Deposited: 25 Jul 2013 10:52
    Last Modified: 25 Jul 2013 10:52
    URI: https://eprints.bbk.ac.uk/id/eprint/7777

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