This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London.Image classification is an active topic of computer vision research. This topic
deals with the learning of patterns in order to allow efficient classification of visual
information. However, most research efforts have focused on 2D image classification.
In recent years, advances of 3D imaging enabled the development of applications and
provided new research directions. In this thesis, we present methodologies and techniques for image classification using 3D image data. We conducted our research focusing on the attributes and
limitations of depth information regarding possible uses. This research led us to the
development of depth feature extraction methodologies that contribute to the representation
of images thus enhancing the recognition efficiency. We proposed a new
classification algorithm that adapts to the need of image representations by implementing
a scale-based decision that exploits discriminant parts of representations.
Learning from the design of image representation methods, we introduced our own
which describes each image by its depicting content providing more discriminative image
representation. We also propose a dictionary learning method that exploits the
relation of training features by assessing the similarity of features originating from
similar context regions. Finally, we present our research on deep learning algorithms
combined with data and techniques used in 3D imaging. Our novel methods provide
state-of-the-art results, thus contributing to the research of 3D image classificatio
