Evolutionary Computation for Image Content Description applied to image Segmentation and Object Recognition

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@PhdThesis{PerezCastro:thesis,
  author =       "Cynthia Beatriz {Perez Castro}",
  title =        "Evolutionary Computation for Image Content Description
                 applied to image Segmentation and Object Recognition",
  title_sp =     "Computo evolutivo como enfoque en la descripcion del
                 contenido de la imagen aplicado a la segmentacion y el
                 reconocimiento de objetos.",
  school =       "Centro de Investigacion Cientifica y de Educacion
                 Superior de Ensenada",
  year =         "2010",
  type =         "Doctor en Ciencias de la Computacion",
  address =      "Computer Science Department, Km.107 Carretera
                 Tijuana-Ensenada, Ensenada, Baja, California, Mexico",
  month =        jul,
  keywords =     "genetic algorithms, genetic programming, Segmentation,
                 Statistical Descriptors, Co-ocurrence Matrix (GLCM),
                 Local Descriptors, F-Measure, Object Recognition",
  URL =          "http://biblioteca.cicese.mx/catalogo/tesis/ficha.php?id=18586",
  URL =          "http://biblioteca.cicese.mx/catalogos/Ftesis/PerezCastro_thesis.pdf",
  size =         "126 pages",
  abstract =     "The ability to analyse and describe the image content
                 from real or designed scenarios is a challenge and an
                 attractive task for computer vision. This work presents
                 two evolutionary algorithms which analyse the image
                 content for two particular high level tasks such as:
                 image segmentation and object recognition. On the one
                 hand, the image content is analysed using statistical
                 descriptors in a Gray Level Co-ocurrence Matrix (GLCM)
                 in order to achieve good image segmentations. On the
                 other hand, new local descriptor operators are proposed
                 using genetic programming. These operators describe the
                 image content in order to recognise objects localised
                 within indoor and outdoor scenarios presenting
                 different image transformations. First, we present our
                 EvoSeg algorithm, which uses knowledge derived from
                 texture analysis to identify how many homogeneous
                 regions exist in the scene without a priori
                 information. EvoSeg uses texture features derived from
                 the GLMC and optimises a fitness measure, based on the
                 minimum variance criteria, using a hierarchical GA.
                 Later, we include interaction within the EvoSeg
                 optimization process obtaining a new algorithm named
                 EvoSeg. This algorithm complements the chosen texture
                 information with direct human interaction in the
                 evolutionary optimisation process. Interactive
                 evolution helps to improve results by allowing the
                 algorithm to adapt using the new external information
                 based on user evaluation. Finally, we present
                 experimental results using a standard database used for
                 image segmentation from the USC-SIPI (Signal and Image
                 Processing Institute).

                 Second, we describe a genetic programming methodology
                 that synthesises mathematical expressions that are used
                 to improve a well known local descriptor algorithm. It
                 follows the idea that object recognition in the
                 cerebral cortex of primates makes use of features of
                 intermediate complexity that are largely invariant to
                 change in scale, location, and illumination. These
                 local features have been previously designed by human
                 experts using traditional representations that have a
                 clear, preferably mathematically, well-founded
                 definition. However, it is not clear that these same
                 representations are implemented by the natural system
                 with the same representation. Hence, the possibility to
                 design novel operators through genetic programming
                 represents an open research avenue where the
                 combinatorial search of evolutionary algorithms can
                 largely exceed the ability of human experts. Hence, we
                 provide evidence that genetic programming is able to
                 design new features that enhance the overall
                 performance of the best available local descriptor.
                 Experimental results confirm the validity of the
                 proposed approach using a widely accept testbed and an
                 object recognition application for indoor and outdoor
                 scenarios using our best descriptor RDGP2.",
  notes =        "In Spanish.

                 Comite de tesis: Olague Caballero Gustavo; Sucar Succar
                 Luis Enrique; Kelly Martinez Rafael de Jesus; Chernykh
                 Andrey; Torres Rodriguez Jorge;",
}

Genetic Programming entries for Cynthia B Perez

Citations