Chao-Lung Yang1, Yulius Harjoseputro2,3, Yu-Chen Hu4, Yung-Yao Chen2,*
CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4577-4593, 2022, DOI:10.32604/cmc.2022.031305
- 28 July 2022
Abstract This research proposed an improved transfer-learning bird classification framework to achieve a more precise classification of Protected Indonesia Birds (PIB) which have been identified as the endangered bird species. The framework takes advantage of using the proposed sequence of Batch Normalization Dropout Fully-Connected (BNDFC) layers to enhance the baseline model of transfer learning. The main contribution of this work is the proposed sequence of BNDFC that can be applied to any Convolutional Neural Network (CNN) based model to improve the classification accuracy, especially for image-based species classification problems. The experiment results show that the proposed More >
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