An Efficient Method for Automatic Video Annotation and Retrieval in Visual Sensor Networks

Abstract

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