Abstract
Texture classification is a challenging task due to the wide range of natural texture types and large intra-class variations in texture images, such as different rotations, scales, positions and lighting conditions. Many existing methods for extracting texture features are designed carefully by user for specific applications. The extracted texture features are then used as input to various classification methods, such as support vector machines, to classify the textures. The system performance greatly depends on the feature extractor. Unfortunately, there is no systematic approach for feature extractor design. In this paper, we propose a method called extreme learning machine with multi-scale local receptive fields (ELM-MSLRF) to achieve feature learning and classification simultaneously for texture classification. In contrast to traditional methods, the proposed method learns the features by the network itself and can be applied to more general applications. Additionally, it is fast and requires few computations. Experiments on the ALOT texture dataset demonstrate the attractive performance of ELM-MSLRF even compared with the state-of-the-art algorithms. Moreover, the proposed ELM-MSLRF achieves the best performance on the NORB dataset.
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Acknowledgments
This work was supported in part by the NSFC under Grant 61573150, 61573152, 61370185 and 61175114, the NSF of Guangdong under Grant S2012020010945, S2013010013432, S2013010015940 and 2014A030313253, the Innovation Project of Guangdong under Grant 2013KJCX0009.
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Huang, J., Yu, Z.L., Cai, Z. et al. Extreme learning machine with multi-scale local receptive fields for texture classification. Multidim Syst Sign Process 28, 995–1011 (2017). https://doi.org/10.1007/s11045-016-0414-3
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DOI: https://doi.org/10.1007/s11045-016-0414-3