Cascade active learning for SAR image annotation
Shiyong Cui, Mihai P. Datcu, Pierre Blanchart · 2012
In this paper, a novel active learning approach and system incorporating multiple instance learning for SAR image mining and annotation is introduced. Based on a multiscale and hierarchial patch based image representation, a cascade classifier is learned at different levels. At each level of the hierarchy, a SVM classifier is trained based on active learning and the training sample propagation between different levels is achieved through Multiple Instance SVM (MI-SVM). Classification at the higher level is applied only to the positive patches obtained at the previous level, which can significantly reduce the burden of computation in the case of large data set. Performance has been evaluated through a large data set, which shows promising gain not only in accuracy but also in computation.