Key frames extraction based on local features for efficient video summarization

Abstract

Key frames are the most representative images of a video. They are used in different areas in video processing, such as indexing, retrieval and summarization. In this paper we propose a novel approach for key frames extraction based on local feature description. This approach will be used to summarize the salient visual content of videos. First, we start by generating a set of candidate keyframes. Then we detect interest points for all these candidate frames. After that we will compute repeatability between them and stock the repeatability values in a matrix. Finally we will model repeatability table by an oriented graph and the selection of keframe is inspired from shortest path algorithm A*. Realized experiments on challenging videos show the efficiency of the proposed method: it demonstrates that it is able to prevent the redundancy of the extracted key frames and maintain minimum requirements in terms of memory space.