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The effectiveness of Symmetric Positive Definite (SPD) manifold features hasbeen proven in various computer vision tasks. However, due to the non-Euclideangeometry of these features, existing Euclidean machineries cannot be directlyused. In this paper, we tackle the classification tasks with limited trainingdata on SPD manifolds. Our proposed framework, named Manifold Convex ClassModel, represents each class on SPD manifolds using a convex model, andclassification can be performed by computing distances to the convex models. Weprovide three methods based on different metrics to address the optimizationproblem of the smallest distance of a point to the convex model on SPDmanifold. The efficacy of our proposed framework is demonstrated both onsynthetic data and several computer vision tasks including object recognition,texture classification, person re-identification and traffic sceneclassification.

Convex Class Model on Symmetric Positive Definite Manifolds