A unified shot boundary detection framework based on graph partition model

In this paper, we propose a unified shot boundary detection framework by extending the previous work of graph partition model with temporal constraints. To detect both the abrupt transitions (CUTs) and gradual transitions (GTs, excluding fade out/in) in a unified way, we incorporate temporal multi-resolution analysis into the model. Furthermore, instead of ad-hoc thresholding scheme, we construct a novel kind of feature to characterize shot transitions and employ support vector machine (SVM) with active leaning strategy to classify boundaries and non-boundaries. Extensive experiments have been carried out on the platform of TRECVID benchmark. The experimental results show that the proposed framework outperforms some others and achieves satisfactory results.