A Deep CNN Approach with Transfer Learning for Image Recognition
Cristian Iorga, Victor-Emil Neagoe · 2019
This paper presents a model of Deep Convolutional Neural Networks (CNN) based on transfer learning for image recognition. This means to use a Deep CNN system pretrained on the large ImageNet dataset of 14 million images and 1000 classes in order to learn feature selection. The results of the pretraining phase are transferred to the problem of classification for the images belonging to the UC Merced Land Use dataset with 21 classes. As benchmark, we have considered a Deep CNN trained with a fraction of the same UC Merced Land Use dataset containing the test images for classification. The experimental results have pointed out the obvious advantage of the Deep CNN with transfer learning (accuracy of 0.87 using pretraining over 0.46 for fully training on the same dataset).