A new technique for summarizing video sequences through histogram evolution

We present an efficient technique based on histogram evolution for summarizing video sequences to make them more amenable to browsing and retrieval. First, a ground-truth database of videos is generated in which the shot breaks are detected by human subjects and numbered in order. Three types of histogram are then used to capture the characteristics of color content containing in the video frames. The principle components analysis (PCA) method is adopted to reduce the histogram dimensions and form a 2D feature space. Finally, two approaches, frame difference measures and Fuzzy C-means clustering, are employed to extract video shot breaks. Polylines are drawn between the detected shot breaks to show that the histogram of their colors evolves from frame to frame. In comparison with the ground-truth database, the proposed algorithm achieves a surprising high detection accuracy rate. The extensive experiments also demonstrate that the patterns of histogram evolution can be useful to identify the shot break types, such as cut, dissolve, fade-out, fade-in, and wipe.