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Towards Image Classification with Machine Learning Methodologies for Smartphones

School of Engineering, University of Guelph, Guelph, ON N1G 2W1, Canada
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Mach. Learn. Knowl. Extr. 2019, 1(4), 1039-1057; https://doi.org/10.3390/make1040059
Received: 14 September 2019 / Revised: 29 September 2019 / Accepted: 1 October 2019 / Published: 4 October 2019
Recent developments in machine learning engendered many algorithms designed to solve diverse problems. More complicated tasks can be solved since numerous features included in much larger datasets are extracted by deep learning architectures. The prevailing transfer learning method in recent years enables researchers and engineers to conduct experiments within limited computing and time constraints. In this paper, we evaluated traditional machine learning, deep learning and transfer learning methodologies to compare their characteristics by training and testing on a butterfly dataset, and determined the optimal model to deploy in an Android application. The application can detect the category of a butterfly by either capturing a real-time picture of a butterfly or choosing one picture from the mobile gallery. View Full-Text
Keywords: classification of butterfly; deep learning; transfer learning; tensorflow mobile classification of butterfly; deep learning; transfer learning; tensorflow mobile
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Zhu, L.; Spachos, P. Towards Image Classification with Machine Learning Methodologies for Smartphones. Mach. Learn. Knowl. Extr. 2019, 1, 1039-1057.

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