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The importance of wild video based image set recognition is becomingmonotonically increasing. However, the contents of these collected videos areoften complicated, and how to efficiently perform set modeling and featureextraction is a big challenge for set-based classification algorithms. Inrecent years, some proposed image set classification methods have made aconsiderable advance by modeling the original image set with covariance matrix,linear subspace, or Gaussian distribution. As a matter of fact, most of themjust adopt a single geometric model to describe each given image set, which maylose some other useful information for classification. To tackle this problem,we propose a novel algorithm to model each image set from a multi-geometricperspective. Specifically, the covariance matrix, linear subspace, and Gaussiandistribution are applied for set representation simultaneously. In order tofuse these multiple heterogeneous Riemannian manifoldvalued features, thewell-equipped Riemannian kernel functions are first utilized to map them intohigh dimensional Hilbert spaces. Then, a multi-kernel metric learning frameworkis devised to embed the learned hybrid kernels into a lower dimensional commonsubspace for classification. We conduct experiments on four widely useddatasets corresponding to four different classification tasks: video-based facerecognition, set-based object categorization, video-based emotion recognition,and dynamic scene classification, to evaluate the classification performance ofthe proposed algorithm. Extensive experimental results justify its superiorityover the state-of-the-art.

Multiple Riemannian Manifold-valued Descriptors based Image Set Classification with Multi-Kernel Metric Learning