Cross domain boosting for information fusion in heterogeneous sensor-cyber sources

摘要

Image classification is an active research area in signal processing and pattern recognition, with extensively academic and industrial applications. Existing image classification methods usually assumes consistent image source for training and testing which, however, seldom holds for practical applications. We define the scenario of images being captured from diverse sources (e.g. Internet, surveillance cameras, and mobile phones) as heterogeneous sensor-cyber sources (HSCS). We indicate that, information fusion in HSCS is able to make full use of the diversity of images, and thus improves the generalization of classifier. A novel algorithm named Cross Domain Boosting (CD-Boost) is proposed to fuse information in HSCS. Our CD-Boost algorithm has two characteristics, weighted loss objective and manifold smoothness regularization. Concretely, the weighted boosting loss objective can fuse information by assigning different weights to each source sample, and the weights are determined by minimizing the difference between the data distribution in HSCS. Furthermore, when the images are scarce, a manifold smoothness regularization can prevent overfitting and further improve the accuracy. The experimental results on real data demonstrate that our algorithm outperforms existing methods.