Image orientation detection by ensembles of Stochastic CNNs

Abstract

In this paper we deal with the problem of accurately and automatically detecting the orientation of general images, for instance, of holiday snapshots. Detecting image orientation is an easy task for a human being but can be a long and tedious activity during processing and management of digital photos. Several attempts have been made in the design of systems for automated displaying images in their correct orientation, however, this is still an open problem. In this work we exploit the power of deep learning proposing a transfer learning approach that adjusts pre-trained convolutional neural networks to this classification task. We create ensembles of different Convolutional Neural Network models designed by randomly changing the activation functions in all the activation layers of a given network. Along with several known activation functions we also include the novel Soft Learnable activation function in the “random set”. Our resulting ensembles have been extensively evaluated on more than 45,000 images taken from four different public datasets, showing a remarkable performance improvement with respect to other state-of-the-art approaches. All the source code used for this work is freely available at https://github.com/LorisNanni/.

Keywords

• Image orientation detection
• Convolutional neural networks
• Photo orientation
• Deep learning
• Transfer learning