Heritage Image Classification by Convolution Neural Networks

Abstract

In this project, we have explored the use of Convolution Neural Networks for semantic classification of heritage images. Recently four architectures, AlexNet, GoogLeNet, ResNet, and SENet have been proposed. We have tested them with two different learning modalities. Besides conventional training from scratch, we have resorted to pre-trained networks that have been fine-tuned on a huge amount of data in order to avoid overfitting problems and to reduce design/computing time. Experiments on a dataset we have built by ourselves from Heritage repository show that with the help of this step of fine-tuning, one of the simplest (and oldest) networks AlexNet is able to produce similar results than more sophisticated ones as ResNet which is pretty 3 times more consuming in computing time, mainly due to the number of the layer it uses. With this model, we have been able to show that our model can classify in the right way more than 98% of the images belonging to the dataset test. From now, this model can be used to classify the full dataset of the Heritage repository.