Modern deep learning has recently revolutionized
several fields of classic machine learning and computer vision,
such as, scene understanding, natural language processing and
machine translation. The substitution of feature hand-crafting
with automatic feature learning, provides an excellent
opportunity for gaining an in-depth understanding of large-scale
data statistics. Deep neural networks generally train models with
huge numbers of parameters, facilitating efficient search for
optimal and sub-optimal spaces of highly non-convex objective
functions. On the other hand, Fisher discriminant analysis has
been widely employed to impose class discrepancy, for the sake of
segmentation, classification, and recognition tasks. This thesis
bridges between contemporary deep learning and classic
discriminant analysis, to accommodate some important challenges
in visual scene understanding, i.e. semantic segmentation,
texture classification, and object recognition. The aim is to
accomplish specific tasks in some new high-dimensional spaces,
covered by the statistical information of the datasets under
study. Inspired by a new formulation of Fisher discriminant
analysis, this thesis introduces some novel arrangements of
well-known deep learning architectures, to achieve better
performances on the targeted missions. The theoretical
justifications are based upon a large body of experimental work,
and consolidate the contribution of the proposed idea; Deep
Fisher Discriminant Learning, to several challenges in visual
scene understanding
