Generative Probabilistic Models

Westerveld, Thijs and Vries de, Arjen and Jong de, Franciska (2007) Generative Probabilistic Models. In: Multimedia Retrieval. Data-Centric Systems and Applications . Springer Verlag, Berlin, Germany, pp. 177-198. ISBN 9783540728948

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Abstract:	Many content-based multimedia retrieval tasks can be seen as decision theory problems. Clearly, this is the case for classification tasks, like face detection, face recognition, or indoor/outdoor classification. In all these cases a system has to decide whether an image (or video) belongs to one class or another (respectively face or no face; face A, B, or C; and indoor or outdoor). Even the ad hoc retrieval tasks, where the goal is to find relevant documents given a description of an information need, can be seen as a decision theory problem: documents can be classified into relevant and non-relevant classes, or we can treat each of the documents in the collection as a separate class, and classify a query as belonging to one of these. In all these settings, a probabilistic approach seems natural: an image is assigned to the class with the highest probability.3 If some misclassifications are more severe than others, a decision theoretic approach should be taken, and images should be assigned to the class with lowest risk.
Item Type:	Book Section
Copyright:	© 2007 Springer
Faculty:	Electrical Engineering, Mathematics and Computer Science (EEMCS)
Research Group:	Human Media Interaction (HMI)
Link to this item:	http://purl.utwente.nl/publications/63988
Official URL:	http://dx.doi.org/10.1007/978-3-540-72895-5_6
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