News story clustering with fisher embedding

An automatic news story clustering system is presented to facilitate efficient news browsing and summarization. We describe news content by considering both what objects appear and how these objects move in news stories. With Fisher embedding, we respectively encode local features, semantics features, and dense trajectories as Fisher vectors, based on which similarity between news stories can be well evaluated and thus better clustering performance can be obtained. We verify the effectiveness of Fisher encoding, and further show that motion-based features are more effective than appearance-based features through feature analysis.