Fusion of Audio-Visual Features and Statistical Property for Commercial Segmentation

摘要

Commercial segmentation is a primary step of commercial management which is an emerging technology. Relative to general video scene segmentation, commercial segmentation is particular because of dramatic changes in acoustic effect and chromatic composition. Conventional algorithms emphasize on utilizing new audio and visual features to adapt with change over time. In this paper, we have proposed a novel scheme to fuse audio-visual characteristics and statistical property of commercial length to find individual commercial boundaries. First, mid-level descriptors such as Static Shot with Product Information (SSPI) are used to predict the likelihoods of commercial boundary for every shot boundary. And then, Dynamic Programming (DP) refiner with Distribution of Individual Commercial Length (DICL) constraint is applied to find the optimal path of a Markov Chain of these shot boundaries. Experiments on simulated and real datasets show promising results.