Abstract
Data set is a most crucial aspect for the object recognition by the deep learning. The effect of training a deep learning model would be not good with an insufficient quantity of the data set. For example, there are two similar pictures which are captured from a video with a peacock and these two pictures are separated by one second in this video. Results show that the peacock was recognized by the model in the former picture but it failed in the latter picture due to the angle change of the peacock. In order to improve recognition effects of the model, we propose a system based on Gaussian Mixture Model and Speeded Up Robust Features to recognize peacocks in images. We also implement the prototype of this article and conduct a series of experiments to test the proposed solution. Furthermore, experimental results show the scheme did improve the accuracy of the complete training model.
This work was supported by Ministry of Science and Technology (MOST) of Taiwan under Grants MOST 107-2221-E-006-017-MY2, 108-2218-E-006-029, 108-2221-E-034-015-MY2, and 107-2221-E-218-024. This work was also supported by the “Intelligent Service Software Research Center” in STUST and the “Allied Advanced Intelligent Biomedical Research Center, STUST” under Higher Education Sprout Project, Ministry of Education, Taiwan. This work was financially supported by the “Intelligent Manufacturing Research Center” (iMRC) in NCKU from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education in Taiwan.
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Chen, TT. et al. (2020). Improving Accuracy of Peacock Identification in Deep Learning Model Using Gaussian Mixture Model and Speeded Up Robust Features. In: Nguyen, N., Jearanaitanakij, K., Selamat, A., Trawiński, B., Chittayasothorn, S. (eds) Intelligent Information and Database Systems. ACIIDS 2020. Lecture Notes in Computer Science(), vol 12033. Springer, Cham. https://doi.org/10.1007/978-3-030-41964-6_49
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