Improving deep neural networks classification by preprocessing images

ERDMANN, H.ITO, F. T.TAKABAYASHI, D.DOS SANTOS, D. N.2022-01-122022-01-122016ERDMANN, H.; ITO, F. T.; TAKABAYASHI, D.; DOS SANTOS, D. N. Improving deep neural networks classification by preprocessing images. Computational Vision and Medical Image Processing V - Proceedings of 5th Eccomas Thematic Conference on Computational Vision and Medical Image Processing, VipIMAGE, p. 313-319, oct. 2015.https://repositorio.fei.edu.br/handle/FEI/3937© 2016 Taylor & Francis Group, London.The amount of images used to train a classifier has a great impact in the algorithm’s performance. In the domain of e-commerce, most of the dataset is formed by studio images and we demonstrate in this work that by multiplying the amount of images with different transformations can provide a significant boost on the overall performance of the classifier. Moreover, we list the challenges encountered in such task and present the improvements obtained with a classifier trained with additional images that were synthetically generated by applying several transformations on the original dataset.Acesso RestritoImproving deep neural networks classification by preprocessing imagesArtigo de evento10.1201/b19241-53