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With an increasing interest in social media, a large number of Web images have been created on the Internet. Therefore, extracting useful knowledge from a large-scale set of Web images has become a new type of challenge. In this paper, we focus on Web images that have been posted onto the Internet through social media sites. The main objective of this study is to extract the events and track the topics of a document stream that includes Web images. To address this challenge, this paper proposes a novel method for burst detection in a Web-image document stream. The proposed method integrates a clustering technique with Kleinberg's burst detection. To evaluate the proposed method, we used actual tweets from Twitter users. The experimental results show that the proposed method can extract the events and track the topics related to Web images posted on social media sites.