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To handle high dimensional variables in real world, especially multimedia data, dimension reduction techniques provide effective solutions for feature selection which makes the problem easy to deal in a lower dimension subspace. However, the primary problem with traditional dimension reduction method is to estimate intrinsic dimension of manifold supporting the raw data. Since all existing approaches...
The recently proposed covariance region descriptor has been proven robust and versatile for a modest computational cost. The covariance matrix enables efficient fusion of different types of features, where the spatial and statistical properties, as well as their correlation, are characterized. The similarity between two covariance descriptors is measured on Riemannian manifolds. Based on the same...
Visual tracking is a challenging problem, as an object may change its appearance due to pose variations, illumination changes, and occlusions. Many algorithms have been proposed to update the target model using the large volume of available information during tracking, but at the cost of high computational complexity. To address this problem, we present a tracking approach that incrementally learns...
Recently, the covariance region descriptor has been proved robust and versatile for a modest computational cost. It enables efficient fusion of different types of features. Based on the covariance descriptor and the metric on Riemannian manifolds, we develop a robust Bayesian tracking framework via fragments-based representation in this paper. In this framework, the template object is represented...
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