Unsupervised image segmentation based on material reflectance description and saliency.

@PhdThesis\{Vaz2011a,
  author       = "Eduard V\'azquez",
  title        = "Unsupervised image segmentation based on material reflectance description and saliency.",
  school       = "Universitat Aut\`onoma de Barcelona",
  month        = "Oct",
  year         = "2011",
  keywords     = "Image segmentation, saliency, color.",
  abstract     = "Image segmentations aims to partition an image into a set of non-overlapped regions,
called segments. Despite the simplicity of the definition, image segmentation
raises as a very complex problem in all its stages. The definition of segment is still
unclear. When asking to a human to perform a segmentation, this person segments at
different levels of abstraction. Some segments might be a single, well-defined texture
whereas some others correspond with an object in the scene which might including
multiple textures and colors. For this reason, segmentation is divided in bottom-up
segmentation and top-down segmentation. Bottom up-segmentation is problem independent,
that is, focused on general properties of the images such as textures or
illumination. Top-down segmentation is a problem-dependent approach which looks
for specific entities in the scene, such as known objects.
This work is focused on bottom-up segmentation. Beginning from the analysis
of the lacks of current methods, we propose an approach called RAD. Our approach
overcomes the main shortcomings of those methods which use the physics of the
light to perform the segmentation. RAD is a topological approach which describes a
single-material reflectance.
Afterwards, we cope with one of the main problems in image segmentation: non
supervised adaptability to image content. To yield a non-supervised method, we use
a model of saliency yet presented in this thesis. It computes the saliency of the
chromatic transitions of an image by means of a statistical analysis of the images
derivatives. This method of saliency is used to build our final approach of segmentation:
spRAD. This method is a non-supervised segmentation approach.
Our saliency approach has been validated with a psychophysical experiment as
well as computationally, overcoming a state-of-the-art saliency method.
spRAD also outperforms state-of-the-art segmentation techniques as results obtained
with a widely-used segmentation dataset show.",
  keywords     = "Image segmentation, saliency, color.",
  advisor1      = "2",
  url          = "http://cic.uab.cat/Public/Publications/2011/Vaz2011a"
}