A vague set approach for identifying shot transition in videos using multiple feature amalgamation

Abstract Shot boundary detection (SBD) is the preliminary and most significant step in Content Based Video Retrieval (CBVR). As such the effectiveness of a CBVR system depends heavily on reliable detection of shot boundaries. In this work, a simple yet effective technique for amalgamating several distance features extracted from video frames has been proposed. The aim here is to develop a technique which is able to produce a better distance feature from the existing ones by hybridizing several distance metrics. In the proposed model, any number of distance features can be incorporated and fused together. The resultant feature is not only more robust but also immune to features which are inefficient. Robustness of the proposed method is tested by combining several low performing features with the more efficient ones. Several statistical amalgamation functions are also tested for determining the most efficient one in terms of F1 score. The power of vague sets has been harnessed to detect the shot boundaries effectively using the resultant distance feature. The proposed method is proved to be effective by means of the results obtained, which show that multiple feature amalgamation can lead to a hybrid distance feature which performs better than the best feature incorporated for SBD. The proposed technique is analyzed using ANOVA. A comparison with the other existing methods portray the efficacy of the proposed approach. This method can also be applied for other research problems where several features are to be fused together for producing superior results than the ones obtained by individual methods.