A set-level joint sparse representation for image set classification

Traditional image set classification methods measure the similarities between different sets based on the extracted characteristics of each set. Most of these methods build their models on one kind of visual features or a simply concatenated feature vector of several kinds of features. However, due to redundant or irrelevant information, the concatenated features are usually not discriminant or suffer from the curse of dimensionality. Meanwhile, if the sizes of sets are small, or improper features are employed, the useful information will be limited and conflictive. So in this paper, we propose a set-level joint sparse representation classification (SJSRC) model to combine multiple features to accomplish image set classification task. In the SJSRC, the atom-level and concept-level regularization terms are both imposed to obtain robust representations and the images in the same concept are regarded as a whole to optimize the objective on them jointly to strengthen the intra similarities via a set-level regularization. In addition, we adopt two schemes, namely 'Anchor Graph' and 'Regularized Nearest Points (RNP)', to improve computational efficiency and identification rate. Experiments on several benchmark datasets show that our model obtains competitive recognition performance for image set classification.