Informedia at TRECVID 2003: Analyzing and searching broadcast news video

We submitted a number of semantic classifiers, most of which were merely trained on keyframes. We also experimented with runs of classifiers were trained exclusively on text data and relative time within the video, while a few were trained using all available multiple modalities. 1.2 Interactive search This year, we submitted two runs using different versions of the Informedia systems. In one run, a version identical to last year's interactive system was used by five researchers, who split up the topics between themselves. The system interface emphasizes text queries, allowing search across ASR, closed captions and OCR text. The result set can then be manipulated through: • storyboards of images spanning across video story segments • emphasizing matching shots to a user’s query to reduce the image count to a manageable size • resolution and layout under user control • additional filtering provided through shot classifiers such as outdoors, and shots with people, etc. • display of filter count and distribution to guide their use in manipulating storyboard views. In the best-performing interactive run, for all topics a single researcher used an improved version of the system, which allowed more effective browsing and visualization of the results of text queries using

Effect of Recognition Errors on Text Clustering

This paper presents clustering experiments performed over noisy texts (i.e. texts that have been extracted through an automatic process like character or speech recognition). The effect of recognition errors is investigated by comparing clustering results performed over both clean (manually typed data) and noisy (automatic speech transcriptions) versions of the same speech recording corpus

Enhanced access to digital video through visually rich interfaces

An image-rich interface is presented, which emphasizes visual exploration of sets of images representing shots returned from a query or filter against a digital video corpus. This interface, a storyboard of keyframes for multiple video segments, maintains temporal layout, accommodates contextual cues and filtering, supports additional filtering through visual features, and provides a means of drilling down to synchronized points in the associated video. These features allow for effective information retrieval from a video collection, as evidenced by the success achieved in interactive query for the TREC 2002 Video Retrieval Track (TREC-V). This paper introduces TREC-V, discusses the design of the multi-segment storyboard interface, illustrates its use with respect to the TREC-V topics, and presents results and conclusions based on the TREC-V evaluation. 1