Video shot boundary detection using hybrid dual tree complex wavelet transform with Walsh Hadamard transform

Shot boundary detection (SBD) is the initial process in the video analysis, indexing, summarization, and retrieval. Detection of correct transition from a video sequence and the feature extraction and their effectiveness of presenting the visual content of the video frames are the main factors in SBD. In this paper, the Hybrid Dual-Tree Complex Wavelet Transform with Walsh Hadamard transform (DTCWT-WHT) and the optimized Deep belief network (DBN) are proposed for the SBD. A new feature extraction technique is developed for extracting the feature vector from each block of the image frames. Preprocessing is the initial step to remove the illumination noise in the video frames. In preprocessing, the Fast Averaging Peer Group filter is designed, and the significant feature of this filter is the computational efficiency. After preprocessing, the distance of the adjacent frame is computed using HSV color histogram distance. Hybrid approach is proposed to extract feature and the edge boundaries from the frames, and the continuity signal is constructed. Then, the extracted features are fed to the DBN for the classification process. Social ski driver optimization algorithm (SSDOA) is utilized to update the weights of DBN. Finally, this proposed method detects the abrupt (cut) and gradual (fade in and fade out) transitions from the video frames. Four well-known datasets such as TRECVID 2016, 2017, 2018 and 2019 datasets are utilized to examine the proposed framework. The capability of proposed work is reinforced by performing the comparison with the recent techniques. The experimental outcomes showed the efficiency of the proposed framework by comparing with the existing techniques.