Shot boundary detection using temporal statistics modeling

Abstract

In multimedia information retrieval, shot boundary detection is a very active research topic. In order to perform shot boundary detection, we propose an algorithm for modeling temporal statistics using a novel eigenspace updating method. The feature extracted from the current frame is compared with a model trained from features in the previous frames. A shot boundary is detected if the new feature does not fit well to the existing model. The model is based on principal component analysis (PCA), or the eigenspace method, in which the eigenspace can be updated to capture the non-stationary statistics of the features. The experiment results show that the proposed algorithm outperforms the traditional direct differencing method.