Item

Abstract

Object class recognition is an active topic in computer vision still
presenting many challenges. In most approaches, this task is addressed
by supervised learning algorithms that need a large quantity of labels
to perform well. This leads either to small datasets (< 10,000 images)
that capture only a subset of the real-world class distribution (but
with a controlled and verified labeling procedure), or to large datasets
that are more representative but also add more label noise. Therefore,
semi-supervised learning is a promising direction. It requires only
few labels while simultaneously making use of the vast amount of
images available today. We address object class recognition with
semi-supervised learning. These algorithms depend on the underlying
structure given by the data, the image description, and the similarity
measure, and the quality of the labels. This insight leads to the
main research questions of this thesis: Is the structure given by
labeled and unlabeled data more important than the algorithm itself?
Can we improve this neighborhood structure by a better similarity
metric or with more representative unlabeled data? Is there a connection
between the quality of labels and the overall performance and how
can we get more representative labels? We answer all these questions,
i.e., we provide an extensive evaluation, we propose several graph
improvements, and we introduce a novel active learning framework
to get more representative labels.