Beyond Shot Retrieval

Searching for Broadcast News Items Using Language Models of Concepts

by Robin Aly, Aiden Doherty, Djoerd Hiemstra, and Alan Smeaton

Current video search systems commonly return video shots as results. We believe that users may better relate to longer, semantic video units and propose a retrieval framework for news story items, which consist of multiple shots. The framework is divided into two parts: (1) A concept based language model which ranks news items with known occurrences of semantic concepts by the probability that an important concept is produced from the concept distribution of the news item and (2) a probabilistic model of the uncertain presence, or risk, of these concepts. In this paper we use a method to evaluate the performance of story retrieval, based on the TRECVID shot-based retrieval groundtruth. Our experiments on the TRECVID 2005 collection show a significant performance improvement against four standard methods.

The paper will be presented at the 32nd European Conference on Information Retrieval (ECIR) in Milton Keynes, UK. (and in the DB colloquium of 24 March)

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