Generative Probabilistic Models


Westerveld, Thijs and Vries, Arjen de and Jong, Franciska de (2007) Generative Probabilistic Models. In: Henk M. Blanken & Henk Ernst Blok & Ling Feng & Arjen P. de Vries (Eds.), 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
Electrical Engineering, Mathematics and Computer Science (EEMCS)
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