The fundamental step in video content analysis is the temporal segmentation of video stream into shots, which is known as Shot Boundary Detection (SBD). The sudden transition from one shot to another is known as Abrupt Transition (AT), whereas if the transition occurs over several frames, it is called Gradual Transition (GT). A unified framework for the simultaneous detection of both AT and GT have been proposed in this article. The proposed method uses the multiscale geometric analysis of Non-Subsampled Contourlet Transform (NSCT) for feature extraction from the video frames. The dimension of the feature vectors generated using NSCT is reduced through principal component analysis to simultaneously achieve computational efficiency and performance improvement. Finally, cost efficient Least Squares Support Vector Machine (LS-SVM) classifier is used to classify the frames of a given video sequence based on the feature vectors into No-Transition (NT), AT and GT classes. A novel efficient method of training set generation is also proposed which not only reduces the training time but also improves the performance. The performance of the proposed technique is compared with several state-of-the-art SBD methods on TRECVID 2007 and TRECVID 2001 test data. The empirical results show the effectiveness of the proposed algorithm.