TV News Story Segmentation Based on Semantic Coherence and Content Similarity

In this paper, we introduce and evaluate two novel approaches, one using video stream and the other using close-caption text stream, for segmenting TV news into stories. The segmentation of the video stream into stories is achieved by detecting anchor person shots and the text stream is segmented into stories using a Latent Dirichlet Allocation (LDA) based approach. The benefit of the proposed LDA based approach is that along with the story segmentation it also provides the topic distribution associated with each segment. We evaluated our techniques on the TRECVid 2003 benchmark database and found that though the individual systems give comparable results, a combination of the outputs of the two systems gives a significant improvement over the performance of the individual systems

Indexing and Retrieval of Digital Video Sequences based on Automatic Text Recognition

Efficient indexing and retrieval of digital video is an importantaspect of video databases. One powerful index for retrieval is the text appearing in them. It enables content- based browsing. We present our methods for automatic segmentation and recognition of text in digital videos. The algorithms we propose make use of typical characteristics of text in videos in order to enable and enhance segmentation and recognition performance. Especially the inter-frame dependencies of the characters provide new possibilities for their refinement. Then, a straightforward indexing and retrieval scheme is introduced. It is used in the experiments to demonstrate that the proposed text segmentation and text recognition algorithms are suitable for indexing and retrieval of relevant video scenes in and from a video data base. Our experimental results are very encouraging and suggest that these algorithms can be used in video retrieval applications as well as to recognize higher semantics in video

Automatic text segmentation and text recognition for video indexing

Efficient indexing and retrieval of digital video is an important function of video databases. One powerful index for retrieval is the text appearing in them. It enables content-based browsing. We present our methods for automatic seg-mentation of text in digital videos. The output is directly passed to a standard OCR software package in order to translate the segmented text into ASCII. The algorithms we propose make use of typical characteristics of text in videos in order to enable and enhance segmentation performance. Especially the inter-frame dependencies of the characters provide new possibilities for their refinement. Then, a straightforward indexing and retrieval scheme is intro-duced. It is used in the experiments to demonstrate that the proposed text segmentation algorithms together with exist-ing text recognition algorithms are suitable for indexing and retrieval of relevant video sequences in and from a video database. Our experimental results are very encouraging and suggest that these algorithms can be used in video retrieval applications as well as to recognize higher seman-tics in videos

On User Modelling for Personalised News Video Recommendation

In this paper, we introduce a novel approach for modelling user interests. Our approach captures users evolving information needs, identifies aspects of their need and recommends relevant news items to the users. We introduce our approach within the context of personalised news video retrieval. A news video data set is used for experimentation. We employ a simulated user evaluation

VIDEO SCENE DETECTION USING CLOSED CAPTION TEXT

Issues in Automatic Video Biography Editing are similar to those in Video Scene Detection and Topic Detection and Tracking (TDT). The techniques of Video Scene Detection and TDT can be applied to interviews to reduce the time necessary to edit a video biography. The system has attacked the problems of extraction of video text, story segmentation, and correlation. This thesis project was divided into three parts: extraction, scene detection, and correlation. The project successfully detected scene breaks in series television episodes and displayed scenes that had similar content

Uncertainty-aware video visual analytics of tracked moving objects

Vast amounts of video data render manual video analysis useless while recent automatic video analytics techniques suffer from insufficient performance. To alleviate these issues we present a scalable and reliable approach exploiting the visual analytics methodology. This involves the user in the iterative process of exploration hypotheses generation and their verification. Scalability is achieved by interactive filter definitions on trajectory features extracted by the automatic computer vision stage. We establish the interface between user and machine adopting the VideoPerpetuoGram (VPG) for visualization and enable users to provide filter-based relevance feedback. Additionally users are supported in deriving hypotheses by context-sensitive statistical graphics. To allow for reliable decision making we gather uncertainties introduced by the computer vision step communicate these information to users through uncertainty visualization and grant fuzzy hypothesis formulation to interact with the machine. Finally we demonstrate the effectiveness of our approach by the video analysis mini challenge which was part of the IEEE Symposium on Visual Analytics Science and Technology 2009