Automatic video annotation has become an important issue in visual sensor networks, due to the existence of a semantic gap.\nAlthough it has been studied extensively, semantic representation of visual information is not well understood. To address the\nproblem of pattern classification in video annotation, this paper proposes a discriminative constraint to find a solution to approach\nthe sparse representative coefficients with discrimination.We study a general method of discriminative dictionary learning which\nis independent of the specific dictionary and classifier learning algorithms. Furthermore, a tightly coupled discriminative sparse\ncoding model is introduced. Ultimately, the experimental results show that the provided method offers a better video annotation\nmethod that cannot be achieved with existing schemes.
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