Shot Boundary Detection Using Artificial Neural Network

Abstract

AbstractAs of late video is the most utilized information type on the Internet. Content, sound, and pictures are consolidated to establish a video, so recordings are enormous in size. The human mind can process visual media quicker than it can process message. This expansion in information has required the investigation of powerful strategies to process and store information content. In this paper, we have suggested a hybrid video shot boundary detection process using feature extraction by mean log difference which is combined with artificial neural network. We devised two-step method for automatic shot boundary detection. Firstly, features are extracted using H, V, S procedure along with histogram distribution technique, and then, this mean log difference array is applied as an input to ANN which identifies video shots based on probability function. We have incorporated feed-forward network structure which processes nonlinear factual information to calculate shot boundary detection considering probability function. Finally, we have evaluated the results using precision, recall, and F1 measure. An experimental result indicates that ANN along with mean log difference, it offers efficient representation of shot boundaries and the results are satisfactory. Comparing the proposed method with improved block color feature method, there is a sort of trade-off relation between the two algorithms, and it is observed that for fast characteristic variations, ANN performs moderately better while for complex videos improved block color feature method is suited in better way.KeywordsArtificial neural network (ANN)Content-based image retrieval (CBIR)Shot boundary detection (SBD)Mean log difference