@phdthesis{6376,
  title = {Kernel Learning Approaches for Image Classification},
  abstract = {This thesis extends the use of kernel learning techniques to specific problems
  of image classification. Kernel learning is a paradigm in the field of machine
  learning that generalizes the use of inner products to compute similarities between
  arbitrary objects. In image classification one aims to separate images
  based on their visual content.
  We address two important problems that arise in this context: learning with
  weak label information and combination of heterogeneous data sources. The
  contributions we report on are not unique to image classification, and apply
  to a more general class of problems.
  We study the problem of learning with label ambiguity in the multiple instance
  learning framework. We discuss several different image classification
  scenarios that arise in this context and argue that the standard multiple instance
  learning requires a more detailed disambiguation. Finally we review
  kernel learning approaches proposed for this problem and derive a more efficient 
  algorithm to solve them.
  The multiple kernel learning framework is an approach to automatically
  select kernel parameters. We extend it to its infinite limit and present an
  algorithm to solve the resulting problem. This result is then applied in two
  directions. We show how to learn kernels that adapt to the special structure of
  images. Finally we compare different ways of combining image features for object
  classification and present significant improvements compared to previous
  methods.},
  degree_type = {PhD},
  institution = {Universität des Saarlandes, Saarbrücken, Germany},
  school = {Biologische Kybernetik},
  month = oct,
  year = {2009},
  author = {Gehler, PV.},
  month_numeric = {10}
}