Abstract

We present an image set classifification algorithm based on unsupervised clustering of labeled training and unlabeled test data where labels are only used in the stopping criterion. The probability distribution of each class over the set of clusters is used to defifine a true set based similarity measure. To this end, we propose an iterative sparse spectral clustering algorithm. In each iteration, a proximity matrix is effificiently recomputed to better represent the local subspace structure. Initial clusters capture the global data structure and fifiner clusters at the later stages capture the subtle class differences not visible at the global scale. Image sets are compactly represented with multiple Grassmannian manifolds which are subsequently embedded in Euclidean space with the proposed spectral clustering algorithm. We also propose an effificient eigenvector solver which not only reduces the computational cost of spectral clustering by many folds but also improves the clustering quality and fifinal classifification results. Experiments on fifive standard datasets and comparison with seven existing techniques show the effificacy of our algorithm