The Analysis of Sparse Representations for the Sequence of Images of Videos

Sparse representation has become very popular in fields of signal processing, image processing computer vision and pattern recognition. Sparse representation also has good reputation in both theoretical and practical applications. Images can be sparsely coded by structural primitives and recently the sparse coding or sparse representation has been widely used to resolve the problems in image resolution applications by the l₀-norm and l₁-norm techniques. Sparse representation has been used in pattern classification. Sparse coded a set of bases and classified the signal of a signal based on its coding vector. Sparse representation can be used for the face recognition, the face image is first sparsely coded and then classification is performed by checking the least coding error and also for the pattern recognition. In this paper, using the sparse representations for the analysis of the sequence of images. As per this approach, the sparse representations are used detect the error in each frame of the video which is might be have the anomalous events. This method gives the error detection rates and elapsed time for the video by using the sparse representation algorithms.