Abstract
Image classification has been applied in many fields, which is an important branch of computer vision and pattern recognition. The boosting algorithm which is belong to ensemble learning can integrate several homogeneous classifiers, and combine the output layer’s result of every classifier to improve the final classification accuracy. In this paper, the Adaboost-DBN algorithm is used to combine the four weak classifiers (DBN) and construct a strong classifier. The Adaboost-DBN algorithm is based on the Adaboost M1 algorithm and is used to achieve higher classification accuracy. The proposed algorithm is tested on the Corel-1K data set, and the result of classification is significantly improved comparing to other classifiers.
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Acknowledgement
The Humanities and social sciences research projects of the Ministry of Education (18YJAZH128) and the Basic Scientific Research Operating Expense Project of Provincial Institutions of Higher Education in Heilongjiang (17XN003).
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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Sun, H., Tao, W., Wang, R., Ren, C., Zhao, Z. (2019). Research on Image Classification Method Based on Adaboost-DBN. In: Jia, M., Guo, Q., Meng, W. (eds) Wireless and Satellite Systems. WiSATS 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 281. Springer, Cham. https://doi.org/10.1007/978-3-030-19156-6_21
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DOI: https://doi.org/10.1007/978-3-030-19156-6_21
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