The performance of face, voice, and multimodal speaker verification systems in complex and non-controlled scenarios, is typically lower than systems developed in highly controlled environments. With the aim to facilitate the development of robust multi-modal speaker recognition systems, a new multi-modal (audio-visual) Australian broadcast UCBN (University of Canberra Broadcast News) corpus was...

The development of a semantic web of text, images, structured information, and continuous media information including video and audio, depends on being able to annotate content with semantic meaning and then use that content description directly in applications. ANNODEX is a recently announced initiative to allow continuous media files to be integrated directly with their own content descriptio...

Content -based video retrieval (CBVR ) problems have gained significant importance in today’s intelligence world demanding further insight. Compared to the traditional video indexing systems, CBVR systems do not require the intensive human effort in the semantic annotation. In this paper, we propose the PEGASUS system. PEGASUS is an integrated news video search system containing two utilities: ...

In this paper we present NewsFlash, an adaptive search system that assists a searcher to efficiently search a library of stored TV news reports. The system automatically summarises the closed-caption subtitles embedded in the TV broadcasts and selects appropriate sentences to best describe report content in respect to the searcher’s query. During interaction the system selects useful terms from...

We focus on the problem of learning semantics from multimedia data associated with broadcast video documents. In this paper we propose to learn semantic concepts from multimodal sources based on style and context detectors, in combination with statistical classifier ensembles. As a case study we present our method for detecting the concept of news subject monologues. This approach had the best ...

In this work, we tackle the problem of predicting entity popularity on Twitter based on the news cycle. We apply a supervised learning approach and extract four types of features: (i) signal, (ii) textual, (iii) sentiment and (iv) semantic, which we use to predict whether the popularity of a given entity will be high or low in the following hours. We run several experiments on six different ent...

Despite dramatic increases in available political information through cable television and the Internet, political knowledge and turnout have not changed noticeably. To explain this seeming paradox, I argue that greater media choice makes it easier for people to find their preferred content. People who like news take advantage of abundant political information to become more knowledgeable and m...

The present note first provides an alternative formulation of the Cancian, Bills and Bergström (1995)-problem which discards the non-existence difficulty and consequently allows to consider some extensions of the TV-newscast scheduling game.The extension we consider consists in assuming that viewers’preferences between the competing channels do not depend only on the timing of their broadcast, ...

E€ective retrieval and browsing by content of videos is based on the association of high-level information with visual data. Automatic extraction of high-level content descriptors requires to exploit the technical characteristics of video types. This paper comprises a complete system for content-based retrieval and browsing of news reports; the annotation of the video stream is fully automated ...

Copyright © 2008-2009, Usability Professionals‘ Association and the authors. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, or republish, to pos...