Artistic photo filtering recognition using cnns

In this paper we propose an approach based on deep Convolutional Neural Networks (CNNs) to recognize artistic photo filters applied to images. A total of 22 types of Instagram-like filters is considered. Different CNN architectures taken from the image recognition literature are compared on a dataset of more than 0.46 M images from the Places-205 dataset. Experimental results show that not only it is possible to reliably determine whether or not one of these filters has been applied, but also which one. Differently from other tasks, where the fine-tuning of a CNN trained on a different problem is usually good enough, here the fine-tuned AlexNet obtains an accuracy of only 67.5%. We show, instead, that an accuracy of about 99.0% can be obtained by training a CNN from scratch for this specific problem.