Query set centered sparse projection learning for set based image classification

Set based image classification technology has been developed successfully in recent decades. Previous approaches dispose set based image classification by employing all the gallery sets to learn metrics or construct the model using a typical number of parameters. However, they are based on the assumption that the global structure is consistent with the local structure, which is rigid in real applications. Additionally, the participation of all gallery sets increases the influence of outliers. This paper conducts this task via sparse projection learning by employing ℓ 2,1 norm from the perspective of the query set. Instead of involving all the image sets, this work devotes to searching for a local region, which is centered with a query set and constructed by the candidates selected from different classes in the gallery sets. By maximizing the inter-class while minimizing the intra-class of the candidates from the gallery sets from the query set, this work can learn a discriminate and sparse projection for image set feature extraction. In order to learn the projection, an alternative updating algorithm to solve the optimization problem is proposed and the convergence and complexity are analyzed. Finally, the distance is measured in the discriminate low-dimensional space using Euclidean distance between the central data point of the query set and the central one of images from the same class. The proposed approach learns the projection in the local set centered with the query set with ℓ 2,1 norm, which contributes to more discriminative feature. Compared with the existing algorithms, the experiments on the challenging databases demonstrate that the proposed simple yet effective approach obtains the best classification accuracy with comparable time cost.