Shot Boundary Detection in Videos Using Saliency based Statistical Model

Video indexing and retrieval play a vital role in video management systems involving modules such as shot boundary detection, key frame extraction, and feature representation. Among these modules, Video Shot Boundary Detection (VSBD) is the important primary step, over which the entire retrieval performance relies on. In this paper, a new shot boundary detection mechanism based on context-driven saliency map is proposed to detect the transitions in the highly challenging videos with varying lighting effects, object and camera motion. This saliency map detects the salient regions in the frame along with the vital background scenes. The statistical features derived from the saliency map are used to derive the dissimilarity value. The dissimilarity value is compared to the threshold to determine the location and types of transition in the video. To evaluate the proposed framework for VSBD, the benchmark dataset namely TRECVID is used. It is inferred that this saliency-based VSBD approach yields promising results when evaluated on TRECVID and IDV dataset. The average F-Score in detecting the overall transitions on TRECVID dataset is 93.16% and IDV Dataset is 92.23%.