Abstract:Graph embedding discriminant analysis on manifold approach represents each image set as a subspace on manifold. It maps the manifold to a more discriminative one with geometrical structure and local information preserved. However, its accuracy critically depends on the number of local neighbours when constructing similarity graph. In this paper, we present a novel approach with fixed neighbour numbers to implement graph embedding Grassmannian discriminant analysis based on low-rank representation (LRR) for each image set. After recovering the low-rank components of each set, we find that preserving the nearest neighbour structure of nodes with same label and all the different label information during the manifold mapping can always achieve the best performance. Experiments on two image datasets (15-scenes categories and Caltech101) show that the proposed method greatly improves the classification accuracy of image sets.
