Superpixels are small image segments that are used in popular approaches to object
detection and recognition problems. The superpixel approach is motivated by the observation
that pixels within small image segments can usually be attributed the same
label. This allows a superpixel representation to produce discriminative features based
on data dependent regions of support. The reduced set of image primitives produced
by superpixels can also be exploited to improve the efficiency of subsequent processing
steps. However, it is common for the superpixel representation to have a different graph
structure from the original pixel representation of the image.
The first part of the thesis argues that a number of desirable properties of the
pixel representation should be maintained by superpixels and that this is not possible
with existing methods. We propose a new representation, the superpixel lattice, and
demonstrate its advantages.
The second part of the thesis investigates incorporating a priori information into
superpixel segmentations. We learn a probabilistic model that describes the spatial
density of object boundaries in the image. We demonstrate our approach using road
scene data and show that our algorithm successfully exploits the spatial distribution of
object boundaries to improve the superpixel segmentation.
The third part of the thesis presents a globally optimal solution to our superpixel
lattice problem in either the horizontal or vertical direction. The solution makes use of
a Markov Random Field formulation where the label field is guaranteed to be a set of
ordered layers. We introduce an iterative algorithm that uses this framework to learn
colour distributions across an image in an unsupervised manner.
We conclude that our approach achieves comparable or better performance than
competing methods and that it confers several additional advantages