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