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Classical dictionary learning algorithms that rely on a single source of information have been successfully used for the discriminative tasks. However, exploiting multiple sources has demonstrated its effectiveness in solving challenging real-world situations. We propose a new framework for feature fusion to achieve better classification performance as compared to the case where individual sources...
To deal with the problem of view invariant action recognition, this paper presents a novel approach to recognize human actions across cameras via reconstructable paths. Each action is modelled as a bag of visual-words based on the spatio-temporal features. Although this action representation is sensitive to view changes, the proposed reconstructable path is able to “translate” the action descriptor...
This paper presents an algorithm using discriminative sparse representations to segment tissues in optical images of the uterine cervix. Because of the large variations in the image appearance caused by the changing of illumination and specular reflection, the different classes of color and texture features in optical images are often overlapped with each other. Using sparse representations they can...
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