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

Interactive searching and browsing of video archives: using text and using image matching

Over the last number of decades much research work has been done in the general area of video and audio analysis. Initially the applications driving this included capturing video in digital form and then being able to store, transmit and render it, which involved a large effort to develop compression and encoding standards. The technology needed to do all this is now easily available and cheap, with applications of digital video processing now commonplace, ranging from CCTV (Closed Circuit TV) for security, to home capture of broadcast TV on home DVRs for personal viewing. One consequence of the development in technology for creating, storing and distributing digital video is that there has been a huge increase in the volume of digital video, and this in turn has created a need for techniques to allow effective management of this video, and by that we mean content management. In the BBC, for example, the archives department receives approximately 500,000 queries per year and has over 350,000 hours of content in its library. Having huge archives of video information is hardly any benefit if we have no effective means of being able to locate video clips which are of relevance to whatever our information needs may be. In this chapter we report our work on developing two specific retrieval and browsing tools for digital video information. Both of these are based on an analysis of the captured video for the purpose of automatically structuring into shots or higher level semantic units like TV news stories. Some also include analysis of the video for the automatic detection of features such as the presence or absence of faces. Both include some elements of searching, where a user specifies a query or information need, and browsing, where a user is allowed to browse through sets of retrieved video shots. We support the presentation of these tools with illustrations of actual video retrieval systems developed and working on hundreds of hours of video content

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

Evaluation campaigns and TRECVid

The TREC Video Retrieval Evaluation (TRECVid) is an international benchmarking activity to encourage research in video information retrieval by providing a large test collection, uniform scoring procedures, and a forum for organizations interested in comparing their results. TRECVid completed its fifth annual cycle at the end of 2005 and in 2006 TRECVid will involve almost 70 research organizations, universities and other consortia. Throughout its existence, TRECVid has benchmarked both interactive and automatic/manual searching for shots from within a video corpus, automatic detection of a variety of semantic and low-level video features, shot boundary detection and the detection of story boundaries in broadcast TV news. This paper will give an introduction to information retrieval (IR) evaluation from both a user and a system perspective, highlighting that system evaluation is by far the most prevalent type of evaluation carried out. We also include a summary of TRECVid as an example of a system evaluation benchmarking campaign and this allows us to discuss whether such campaigns are a good thing or a bad thing. There are arguments for and against these campaigns and we present some of them in the paper concluding that on balance they have had a very positive impact on research progress

Balancing the power of multimedia information retrieval and usability in designing interactive TV

Steady progress in the field of multimedia information retrieval (MMIR) promises a useful set of tools that could provide new usage scenarios and features to enhance the user experience in today s digital media applications. In the interactive TV domain, the simplicity of interaction is more crucial than in any other digital media domain and ultimately determines the success or otherwise of any new applications. Thus when integrating emerging tools like MMIR into interactive TV, the increase in interface complexity and sophistication resulting from these features can easily reduce its actual usability. In this paper we describe a design strategy we developed as a result of our e®ort in balancing the power of emerging multimedia information retrieval techniques and maintaining the simplicity of the interface in interactive TV. By providing multiple levels of interface sophistication in increasing order as a viewer repeatedly presses the same button on their remote control, we provide a layered interface that can accommodate viewers requiring varying degrees of power and simplicity. A series of screen shots from the system we have actually developed and built illustrates how this is achieved

Glasgow University at TRECVID 2006

In the first part of this paper we describe our experiments in the automatic and interactive search tasks of TRECVID 2006. We submitted five fully automatic runs, including a text baseline, two runs based on visual features, and two runs that combine textual and visual features in a graph model. For the interactive search, we have implemented a new video search interface with relevance feedback facilities, based on both textual and visual features. The second part is concerned with our approach to the high-level feature extraction task, based on textual information extracted from speech recogniser and machine translation outputs. They were aligned with shots and associated with high-level feature references. A list of significant words was created for each feature, and it was in turn utilised for identification of a feature during the evaluation

Indexing, browsing and searching of digital video

Video is a communications medium that normally brings together moving pictures with a synchronised audio track into a discrete piece or pieces of information. The size of a “piece ” of video can variously be referred to as a frame, a shot, a scene, a clip, a programme or an episode, and these are distinguished by their lengths and by their composition. We shall return to the definition of each of these in section 4 this chapter. In modern society, video is ver

Unconstrained Scene Text and Video Text Recognition for Arabic Script

Building robust recognizers for Arabic has always been challenging. We demonstrate the effectiveness of an end-to-end trainable CNN-RNN hybrid architecture in recognizing Arabic text in videos and natural scenes. We outperform previous state-of-the-art on two publicly available video text datasets - ALIF and ACTIV. For the scene text recognition task, we introduce a new Arabic scene text dataset and establish baseline results. For scripts like Arabic, a major challenge in developing robust recognizers is the lack of large quantity of annotated data. We overcome this by synthesising millions of Arabic text images from a large vocabulary of Arabic words and phrases. Our implementation is built on top of the model introduced here [37] which is proven quite effective for English scene text recognition. The model follows a segmentation-free, sequence to sequence transcription approach. The network transcribes a sequence of convolutional features from the input image to a sequence of target labels. This does away with the need for segmenting input image into constituent characters/glyphs, which is often difficult for Arabic script. Further, the ability of RNNs to model contextual dependencies yields superior recognition results.Comment: 5 page