Abstract
Classification of microscopic sandstone images is an essential task in geology, and the classical method is either subjective or time-consuming. Computer aided automatic classification has been proved useful, but it seldom considers the situation where sandstone images are collected from multiple regions. In this paper, we provide Festra, which uses transfer learning to handle the problem of cross-region microscopic sandstone image classification. The method contains two main parts: one includes feature selection and normalization, the other uses an enhanced Tradaboost for instance transfer. Experiments are conducted based on the sandstone images taken from four regions in Tibet to study the performance of Festra. The experimental results have proved both effectiveness and potentials of Festra, which provides competitive prediction performance on all the four regions, with few target instances labeled suitable for the field use.
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Acknowledgements
The authors thank Dr. XiuMian Hu’s research group in Nanjing University for provision of the sandstone images and informative comments. This work is supported by the NSFC Projects under NOs. 61373012, 61321491, and 91218302. This work is partially supported by Collaborative Innovation Center of Novel Software Technology and Industrialization.
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Li, N., Wang, D., Gu, Q., Hao, H., Chen, D. (2016). Festra: A Feature Based Microscopic Sandstone Images Classification Method Using Transfer Learning. In: Zhang, L., Xu, C. (eds) Software Engineering and Methodology for Emerging Domains. NASAC 2016. Communications in Computer and Information Science, vol 675. Springer, Singapore. https://doi.org/10.1007/978-981-10-3482-4_12
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DOI: https://doi.org/10.1007/978-981-10-3482-4_12
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