Journal
NEUROCOMPUTING
Volume 253, Issue -, Pages 70-76Publisher
ELSEVIER
DOI: 10.1016/j.neucom.2016.09.129
Keywords
Heterogenous feature fusion; Image classification; Multiple kernel learning; Score-distribution information
Categories
Funding
- National Nature Science Foundation of China [61672133, 61632007]
- Fundamental Research Funds for the Central Universities [ZYGX2015J058, ZYGX2014Z007]
- Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology
- Priority Academic Program Development of Jiangsu Higher Education Institutions
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Over the past decades, features generated by different models have been designed to describe various aspects of object. To connect the complementary information and represent the data properly, effective heterogeneous feature fusion methods are required. Multiple kernel learning (MKL) methods are widely adopted to learn the feature weights and to fuse features on score-level. In this paper, we exploit score distribution to address the feature fusion problem and propose a novel method named score-distribution MKL (SD-MKL) for image classification. Different from existing MKL methods, SD-MKL uses weights which are learned from score curves as a constraint on the weights of kernels. It contains two stages in offline part: (1) independent data is used to construct reference curves according to classes and feature type; (2) samples and corresponding score-distribution weights are put into multi-kernel support vector machine (MKSVM) to learn feature weights. Our experimental results demonstrate the effect of exploiting score-distribution information on two datasets, which significantly benefits the performance of image classification. (C) 2017 Elsevier B.V. All rights reserved.
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