Identifying key frames using group sparsity analysis

A method for identifying a set of key video frames from a video sequence including a time sequence of video frames, each video frame including an array of image pixels having pixel values, comprising:

• a) selecting a set of video frames from the video sequence;
• b) extracting a feature vector for each video frame in the set of video frames;
• c) applying a group sparsity algorithm including a group sparse solver to represent the feature vector for a particular video frame as a group sparse combination of the feature vectors for the other video frames in the set of video frames, each feature vector for the other video frames in the group sparse combination having an associated weighting coefficient, wherein the weighting coefficients for feature vectors corresponding to other video frames that are most similar to the particular video frame are non-zero, and the weighting coefficients for feature vectors corresponding to other video frames that are most dissimilar from the particular video frame are zero;
• d) analyzing the weighting coefficients to determine a video frame cluster of temporally-contiguous, similar video frames that includes the particular video frame;
• e) repeating steps c)-d) for a plurality of particular video frames to provide a plurality of video frame clusters;
• f) selecting a set of key video frames based on the video frame clusters;
• and
• g) storing an indication of the selected key video frames in a processor-accessible memory;
• wherein the method is performed, at least in part, using a data processor, and wherein the group sparse solver is invoked once for each group, to reduce computational complexity.