The efficient organization and navigation of web videos in the topic level could enhance the user experience and boost the user's understanding about the happened events. Due to the potential application prospects, topic detection attracts increasing research interests in the last decade. On one hand, the user concerned real world hot topic always leads to a massive discussion in the video sharing sites, such as YouTube, Youku, etc. On the other hand, the search volume of the topic related keywords are growing explosively in the search engine such as Google, Yahoo, etc. These keywords are the queries formulated by the users to search their concerned topics. They reflect the users' intention and could be used as a clue to find the hot topics. In this paper, different from the traditional topic detection methods, which mainly rely on data clustering, we propose a novel multi-clue fusion approach for web video topic detection. In our approach, firstly by utilizing the video related tag information, a maximum average score and a burstiness degree are proposed to extract the dense-bursty tag groups. Secondly, the near-duplicate keyframes (NDK) are extracted from the videos and fused with the extracted tag groups. After that, the hot search keywords from the search engine are used as guidance for topic detection. Finally, these clues are combined together to detect the topics hidden in the web video data. Experiment is conducted on the YouTube video data and the results demonstrate that the proposed method is effective.

Robust and fast near-duplicate video detection is an important task with many potential applications. Most existing systems focus on the comparison between full copy videos or partial near-duplicate videos. While it is more challenging to find similar content for videos containing multiple near-duplicate segments at random locations with various connections. In this paper, we propose a new graph based method to detect complex near-duplicate video sub-clips. First, we develop a new succinct video descriptor for keyframe match. Then a graph is established to exploit temporal consistency of matched keyframes. The nodes of the graph are the matched frame pairs; the edge weights are computed from the temporal alignment and frame pair similarities. In this way, the validly matched keyframes would form a dense subgraph whose nodes are strongly connected. This graph model also preserves the complex connections of sub-clips. Thus detecting complex near-duplicate sub-clips is transformed to the problem of finding all the dense subgraphs. We employ the optimization method of graph shift to solve this problem due to its robust performance. The experiments are conducted on the dataset with various transformations and complex temporal relations. The results demonstrate the effectiveness and efficiency of the proposed method.