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Official URL: http://www.springerlink.com/content/rynb7qvdeeymxggq/

Abstract

An increasing number of large publicly available video libraries results in a demand for techniques that can manipulate the video data based on content. In this paper, we present a content-based video retrieval system called Cobra. The system supports automatic extraction and retrieval of high-level concepts (such as video objects and events) from raw video data. It benefits from using domain knowledge, but at the same time, provides a general framework that can be used in different domains.

The contribution of this work is twofold. Firstly, we demonstrate how different knowledge-based techniques can be used together within a single video database management system to interpret low-level video features into semantic content. The system uses spatio-temporal rules, Hidden Markov Models (HMMs), and Dynamic Bayesian Networks (DBNs) to model and recognize video objects and events. Secondly, we show how these techniques can be effectively used for different application domains. In particular, we validate our approach in the domain of tennis and Formula 1 videos.db

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