Transfer deep convolutional activations-based features for domain adaptation in sensor networks

In this paper, we propose a novel method named transfer deep convolutional activation-based features (TDCAF) for domain adaptation in sensor networks. Specifically, we first train a siamese network with weight sharing to map the images from different domains into the same feature space, which can learn domain-invariant information. Since various feature maps in one convolutional layer of the siamese network contain different kinds of information, we propose a novel vertical pooling strategy to aggregate them into one convolutional activation summing map (CASM) which contains the completed information and preserves the spatial information. We stretch the CASM into one feature vector to obtain the TDCAF. Finally, we feed the proposed TDCAF into a Support Vector Machine (SVM) for classification. The proposed TDCAF is validated on three generalized image databases and three cloud databases, and the classification results outperform the other state-of-the-art methods.