Feature-based video key frame extraction for low quality video sequences
We present an approach to key frame extraction for structuring user generated videos on video sharing websites (e. g. YouTube). Our approach is intended to link existing image search engines to video data. User generated videos are, contrary to professional material, unstructured, do not follow any fixed rule, and their camera work is poor. Furthermore, the coding quality is bad due to low resolution and high compression. In a first step, we segment video sequences into shots by detecting gradual and abrupt cuts. Further, longer shots are segmented into subshots based on location and camera motion features. One representative key frame is extracted per subshot using visual attention features, such as lighting, camera motion, face, and text appearance. These key frames are useful for indexing and for searching similar video sequences using MPEG-7 descriptors [1].