Key-Frame Extraction Using Weighted Multi-view Convex Mixture Models and Spectral Clustering

Reliable video summarization is one of the most important problems in digital video processing and analysis. The most common approach used for shot representation is the extraction of a set of key-frames sufficiently representing the total content of the shot. In such way, the whole video content can be represented using only a few, cautiously picked, non redundant key-frames maintaining at the same time a great percentage of information. A typical approach is to extract key frames using clustering. However, using a single image descriptor to extract key-frames is not sufficient due to large variations in the visual content of videos. In our approach, a weighted multi-view clustering algorithm is employed to combine two different image descriptors into a single similarity matrix, that serves as an input to a spectral clustering algorithm. Each image descriptor (view) does not contribute equally to the similarity matrix, but the weighted multi-view clustering algorithm associates a weight with each view and learns these weights automatically. Numerical experiments using a variety of videos demonstrate that our method is capable of efficiently summarizing video shots regardless of the characteristics of the visual content of the video.