Automatically discovering repetitive clips from large video database is a challenging problem due to the enormous computational cost involved in exploring the huge solution space. Without any a priori knowledge of the contents, lengths and total number of the repetitive clips, we need to discover all of them in the video database. To address the large computational cost, we propose a novel method which translates repetitive clip mining to the continuous path finding problem in a matching trellis, where sequence matching can be accelerated by taking advantage of the temporal redundancies in the videos. By applying the locality sensitive hashing (LSH) for efficient similarity query and the proposed continuous path finding algorithm, our method is of only quadratic complexity of the database size. Experiments conducted on a 10.5-hour TRECVID news dataset have shown the effectiveness, which can discover repetitive clips of various lengths and contents in only 25 minutes, with features extracted off-line.

This paper describes a speaker detection system using cross-modal association methods. Four association approaches are designed using linear and nonlinear association models. Speaker detection experiments were conducted to compare the approaches

Over-segmentation could be relieved by adopting a divisive image segmentation model. This also requires the binary classification of whether a segmented region corresponds to a single semantic object. In this paper, we propose a model to address this classification problem, by detecting if a region contains both "background" and "foreground" regions. When "background" and "foreground" both present, the region is considered to have multiple objects, otherwise it corresponds to a single object. We implement the model based on certain image features of the region that effectively tell the difference between "background" and "foreground". Experiments show that our model can effectively perform the classification tasks.