| dc.description.abstract | The work presented in this thesis is focused at associating a semantics
to the content of an image, linking the content to high level
semantic categories. The process can take place at two levels: either
at image level, towards image categorisation, or at pixel level, in se-
mantic segmentation or semantic labelling. To this end, an analysis
framework is proposed, and the different steps of part (or patch) extraction,
description and probabilistic modelling are detailed. Parts of
different nature are used, and one of the contributions is a method to
complement information associated to them. Context for parts has to
be considered at different scales. Short range pixel dependences are accounted
by associating pixels to larger patches. A Conditional Random
Field, that is, a probabilistic discriminative graphical model, is used
to model medium range dependences between neighbouring patches.
Another contribution is an efficient method to consider rich neighbourhoods
without having loops in the inference graph. To this end, weak
neighbours are introduced, that is, neighbours whose label probability
distribution is pre-estimated rather than mutable during the inference.
Longer range dependences, that tend to make the inference problem
intractable, are addressed as well. A novel descriptor based on local
histograms of visual words has been proposed, meant to both complement
the feature descriptor of the patches and augment the context
awareness in the patch labelling process. Finally, an alternative approach
to consider multiple scales in a hierarchical framework based
on image pyramids is proposed. An image pyramid is a compositional
representation of the image based on hierarchical clustering. All the
presented contributions are extensively detailed throughout the thesis,
and experimental results performed on publicly available datasets are
reported to assess their validity. A critical comparison with the state
of the art in this research area is also presented, and the advantage in
adopting the proposed improvements are clearly highlighted. | en_US |
