The aim of a video summarization system is to provide a set of key frames which contain the most important parts of video. This method results in efficient storage, quick browsing, and retrieval of video collection. In this paper, we propose a new summarization system which firstly divides the video into meaningful shots using an innovative and fast method, and then we sample the video frames of each shot. This results in 97% reduction in under-process video frames. Then, using various characteristics of sampled frames such as color histogram, correlation and moment of inertia, we propose an adaptive aggregation function for combination of these characteristics (differences) and extraction of key frames. The proposed system is evaluated using 250 manual key frames constructed by human operators from 50 downloaded videos. The obtained results show that the proposed system provides better results compared to 6 different traditional methods.
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