@MISC{Barla05ageneral, author = {Annalisa Barla}, title = {A General Framework for Image Kernel Engineering}, year = {2005} }

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Abstract

Understanding image content is a long standing problem of computer science. Despite decades of research in computer vision, an effective solution to this problem does not appear to be in sight. Recent advances in the theory of learning by examples indicate that devising systems which can be trained instead of programmed to solve this problem is an interesting alternative to solutions constructed from higher level image analysis and description. In this thesis we consider a number of image understanding problems viewed as classification problems for which a certain number of input/output pairs is given. Within the statistical learning schemes we adopt (binary support vector machines and one-class support vector machines), the solution to each problem is written as a linear combination of certain functions, named kernel functions. These functions, which satisfy some specific mathematical properties, are evaluated on input pairs and encode the prior knowledge on the problem domain. Roughly speaking, kernel functions can be thought of as measuring the similarity between input pairs by extracting certain features from the raw data.