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.
|Title of host publication||Multimedia Retrieval|
|Editors||Henk Blanken, H.E. Blok, A.P. de Vries, L. Feng|
|Place of Publication||Berlin Heidelberg|
|Number of pages||22|
|Publication status||Published - 2007|
|Name||Data-Centric Systems and Applications|
Westerveld, T. H. W., & de Jong, F. M. G. (2007). Generative Probabilistic Models. In H. Blanken, H. E. Blok, A. P. de Vries, & L. Feng (Eds.), Multimedia Retrieval (pp. 177-198). [10.1007/978-3-540-72895-5_6] (Data-Centric Systems and Applications; No. 2). Berlin Heidelberg: Springer. https://doi.org/10.1007/978-3-540-72895-5_6