Interactive feature learning from SAR image patches

Feature learning algorithms aim to provide a compact and discriminative representation of complex datasets in order to increase the speed and accuracy of clustering or classification. In this paper, we propose a novel interactive feature learning approach which is mainly based on 3D interactive data visualization and Non-negative Matrix Factorization (NMF). Here, the data is visualized in a 3D interface to support human-data interaction. The user interactions are exploited in an NMF framework to learn a compact representation of the data. The conducted experiments on Synthetic Aperture Radar (SAR) images confirm the efficiency of the proposed approach.