Collaborative statistical learning with rough feature reduction for visual target classification

To implement visual target classification, this paper proposes a collaborative statistical learning algorithm for online support vector machine(SVM) classifier learning in wireless multimedia sensor network (WMSN). For achieving robust target classification, classifier learning should be carried out iteratively for updating classifiers according to various situations. Because only unlabeled samples can be acquired, semi-supervised learning is desired to make full use of unlabeled samples. According to the restrict limitation in energy and bandwidth, the proposed algorithm incrementally implement classifier learning with the selected features from multiple sensor nodes, where rough set based feature reduction is used for retaining most of the intrinsic information. Furthermore, some metrics are introduced to evaluate the effectiveness of the samples in specific sensor nodes, and a sensor node selection strategy is also proposed to reduce the impact of inevitable missing detection and false detection. Experimental results demonstrate that the collaborative statistical learning algorithm can effectively implement target classification in WMSN. With the rough set based feature reduction, the proposed algorithm has outstanding performance in energy efficiency and time cost.