&lt;title&gt;Indexing and retrieval of multimedia objects at different levels of granularity&lt;/title&gt;

Faudemay, Pascal; Durand, Gwenaël; Seyrat, Claude; Tondre, Nicolas

doi:10.1117/12.325848

Cited by 2 publications

(1 citation statement)

References 17 publications

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“…For example, the NewsDude news filtering system is a recommender system that suggests text-based news stories that the user might like to read [Billsus and Pazzani 1999] by employing supervised machine learning techniques to induce a classifier that can discriminate between items that are likely to be of interest to the user, and those that are not. Alternatively, in the broadcast news domains the underlying concept of personalising news presentation has been addressed in a limited capacity by some news segmentation systems [Merlino et al 1997;Faudemay et al 1998;Haggerty et al 2003]. For example, Merialdo et al [1999] look at the idea of dynamically generating a storyboard of selected news stories from broadcast TV news for individual users by analysing the news story contents relative to learned user profiles.…”

Section: News Story Recommendation/alertingmentioning

confidence: 99%

User evaluation of Físchlár-News

Lee

Smeaton

O’Connor

et al. 2006

ACM Trans. Inf. Syst.

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Technological developments in content-based analysis of digital video information are undergoing much progress, with ideas for fully automatic systems now being proposed and demonstrated. Yet because we do not yet have robust operational video retrieval systems that can be deployed and used, the usual HCI practise of conducting a usage study and an informed iterative system design is thus not possible. Físchlár-News is one of the first automatic, content-based broadcast news analysis and archival systems that process broadcast news video so that users can search, browse, and play it in an easy-to-use manner with a conventional web browser. The system incorporates a number of state-of-the-art research components, some of which are not yet considered mature technology, yet it has been built to be robust enough to be deployed to users who are interested in access to daily news throughout a university campus. In this article we report and discuss a user-evaluation study conducted with 16 users, each of whom utilized the system freely for a one month period. Results from a detailed qualitative analysis are presented, looking at collected questionnaires, incident diaries, and interaction-log data. The findings suggest that our users employed the system in conjunction with their other news update methods, such as watching TV news at home and browsing online news websites at their workplace, their major concerns being up-to-dateness and coverage of the news content. They tried to accommodate the system to fit their established web browsing habits, and they found local news content and the ability to play selfcontained news stories on their desktop as major values of the system. Our study also resulted in a detailed wishlist of new features which will help in the further development of both our and others' systems.

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