Classification of MPEG VBR Video Data Using Gradient-Based FCM with Divergence Measure

摘要

An efficient approximation of the Gaussian Probability Density Function (GPDF) is proposed in this paper. The proposed algorithm, called the Gradient-Based FCM with Divergence Measure (GBFCM (DM)), employs the divergence measurement as its distance measure and utilizes the spatial characteristics of MPEG VBR video data for MPEG data classification problems. When compared with conventional clustering and classification algorithms such as the FCM and GBFCM, the proposed GBFCM(DM) successfully finds clusters and classifies the MPEG VBR data modelled by the 12-dimensional GPDFs.