Dublin City University video track experiments for TREC 2003

In this paper, we describe our experiments for both the News Story Segmentation task and Interactive Search task for TRECVID 2003. Our News Story Segmentation task involved the use of a Support Vector Machine (SVM) to combine evidence from audio-visual analysis tools in order to generate a listing of news stories from a given news programme. Our Search task experiment compared a video retrieval system based on text, image and relevance feedback with a text-only video retrieval system in order to identify which was more effective. In order to do so we developed two variations of our Físchlár video retrieval system and conducted user testing in a controlled lab environment. In this paper we outline our work on both of these two tasks

News story segmentation in the Físchlár video indexing system

This paper presents an approach to segmenting individual news stories in broadcast news programmes. The approach first performs shot boundary detection and keyframe extraction on the programme. Shots are then clustered into groups based on their colour and temporal similarity. The clustering process is controlled using the groups' statistics. After clustering, a set of criteria are applied and groups are successively eliminated in order to converge upon a set of anchorperson groups. The temporal locations of the shots in these anchorperson groups are then used to segment the programme in terms of individual news items. This work is carried out within the context of a complete video indexing, browsing and retrieval syste

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

TREC video retrieval evaluation: a case study and status report

The TREC Video Retrieval Evaluation is a multiyear, international effort, funded by the US Advanced Research and Development Agency (ARDA) and the National Institute of Standards and Technology (NIST) to promote progress in content-based retrieval from digital video via open, metrics-based evaluation. Now beginning its fourth year, it aims over time to develop both a better understanding of how systems can effectively accomplish such retrieval and how one can reliably benchmark their performance. This paper can be seen as a case study in the development of video retrieval systems and their evaluation as well as a report on their status to-date. After an introduction to the evolution of the evaluation over the past three years, the paper reports on the most recent evaluation TRECVID 2003: the evaluation framework — the 4 tasks (shot boundary determination, high-level feature extraction, story segmentation and typing, search), 133 hours of US television news data, and measures —, the results, and the approaches taken by the 24 participating groups

A generic news story segmentation system and its evaluation

The paper presents an approach to segmenting broadcast TV news programmes automatically into individual news stories. We first segment the programme into individual shots, and then a number of analysis tools are run on the programme to extract features to represent each shot. The results of these feature extraction tools are then combined using a support vector machine trained to detect anchorperson shots. A news broadcast can then be segmented into individual stories based on the location of the anchorperson shots within the programme. We use one generic system to segment programmes from two different broadcasters, illustrating the robustness of our feature extraction process to the production styles of different broadcasters

The Físchlár-News-Stories system: personalised access to an archive of TV news

The “Físchlár” systems are a family of tools for capturing, analysis, indexing, browsing, searching and summarisation of digital video information. Físchlár-News-Stories, described in this paper, is one of those systems, and provides access to a growing archive of broadcast TV news. Físchlár-News-Stories has several notable features including the fact that it automatically records TV news and segments a broadcast news program into stories, eliminating advertisements and credits at the start/end of the broadcast. Físchlár-News-Stories supports access to individual stories via calendar lookup, text search through closed captions, automatically-generated links between related stories, and personalised access using a personalisation and recommender system based on collaborative filtering. Access to individual news stories is supported either by browsing keyframes with synchronised closed captions, or by playback of the recorded video. One strength of the Físchlár-News-Stories system is that it is actually used, in practice, daily, to access news. Several aspects of the Físchlár systems have been published before, bit in this paper we give a summary of the Físchlár-News-Stories system in operation by following a scenario in which it is used and also outlining how the underlying system realises the functions it offers

Segmenting broadcast news streams using lexical chains

In this paper we propose a course-grained NLP approach to text segmentation based on the analysis of lexical cohesion within text. Most work in this area has focused on the discovery of textual units that discuss subtopic structure within documents. In contrast our segmentation task requires the discovery of topical units of text i.e. distinct news stories from broadcast news programmes. Our system SeLeCT first builds a set of lexical chains, in order to model the discourse structure of the text. A boundary detector is then used to search for breaking points in this structure indicated by patterns of cohesive strength and weakness within the text. We evaluate this technique on a test set of concatenated CNN news story transcripts and compare it with an established statistical approach to segmentation called TextTiling