Convolutional Network Features For Scene Recognition

Convolutional neural networks have recently been used to obtain record-breaking results in many vision benchmarks. In addition, the intermediate layer activations of a trained network when exposed to new data sources have been shown to perform very well as generic image features, even when there are substantial differences between the original training data of the network and the new domain. In this paper, we focus on scene recognition and show that convolutional networks trained on mostly object recognition data can successfully be used for feature extraction in this task as well. We train a total of four networks with different training data and architectures, and show that the proposed method combining multiple scales and multiple features obtains state-of-the-art performance on four standard scene datasets.