Recognizing multiple observations using adaptive graph based label propagation

Recently, we introduced a robust and adaptive method for constructing sparse graphs. This method was termed Two Phase Weighted Regularized Least Square (TPWRLS) [6]. In this framework, the graph structure and its affinity matrix are simultaneously computed through a two phase sample coding. The second phase of coding utilizes adaptive sample pruning and re-weighting. In the context of graph-based semi-supervised label propagation, the obtained graph can achieve or outperform state-of-the art graph construction methods. In this paper, we present a performance study of the proposed method by considering two main aspects that were not addressed before. First, the new graph is exploited in order to tackle the problem of recognizing multiple images corresponding to the same category-a non straightforward scenario for supervised recognition techniques. Second, a performance evaluation on different image descriptor types is carried out. Experiments are conducted on three public image datasets: two face datasets and one handwritten digit dataset. These experiments show that in addition to its superiority over competing graph construction methods, the proposed method can easily solve the label inference of multiple observations and can work with several types of image descriptors and scenes.