Genetic Programming for Supervised Figure-ground Image Segmentation

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

@PhdThesis{Liang:thesis,
  author =       "Yuyu Liang",
  title =        "Genetic Programming for Supervised Figure-ground Image
                 Segmentation",
  school =       "Computer Science, Victoria University of Wellington",
  year =         "2018",
  address =      "New Zealand",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://hdl.handle.net/10063/6923",
  URL =          "http://researcharchive.vuw.ac.nz/bitstream/handle/10063/6923/thesis_access.pdf",
  size =         "266 pages",
  abstract =     "Figure-ground segmentation is a process of separating
                 regions of interest from unimportant backgrounds. It is
                 essential to various applications in computer vision
                 and image processing, e.g. object tracking and image
                 editing, as they are only interested in certain regions
                 of an image and use figure-ground segmentation as a
                 pre-processing step. Traditional figure-ground
                 segmentation methods often require heavy human workload
                 (e.g. ground truth labelling), and/or rely heavily on
                 human guidance (e.g. locating an initial model),
                 accordingly cannot easily adapt to diverse image
                 domains. Evolutionary computation (EC) is a family of
                 algorithms for global optimisation, which are inspired
                 by biological evolution. As an EC technique, genetic
                 programming (GP) can evolve algorithms automatically
                 for complex problems without predefining solution
                 models. Compared with other EC techniques, GP is more
                 flexible as it can use complex and variable length
                 representations (e.g. trees) of candidate solutions. It
                 is hypothesised that this flexibility of GP makes it
                 possible to evolve better solutions than those designed
                 by experts. However, there have been limited attempts
                 at applying GP to figure ground segmentation. In this
                 thesis, GP is enabled to successfully address
                 figure-ground segmentation through evolving well
                 performing segmentors and generating effective
                 features. The objectives are to investigate various
                 image features as inputs of GP, develop multiobjective
                 approaches, develop feature selection/construction
                 methods, and conduct further evaluations of the
                 proposed GP methods. The following new methods have
                 been developed. Effective terminal sets of GP are
                 investigated for figureground segmentation, covering
                 three general types of image features, i.e.
                 colour/brightness, texture and shape features. Results
                 show that texture features are more effective than
                 intensities and shape features as they are
                 discriminative for different materials that foreground
                 and background regions normally belong to (e.g. metal
                 or wood). Two new multi-objective GP methods are
                 proposed to evolve figure-ground segmentors, aiming at
                 producing solutions balanced between the segmentation
                 performance and solution complexity. Compared with a
                 reference method that does not consider complexity and
                 a parsimony pressure based method (a popular bloat
                 control technique), the proposed methods can
                 significantly reduce the solution size while achieving
                 similar segmentation performance based on the
                 Mann-Whitney U-Test at the significance level 5percent.
                 GP is introduced for the first time to conduct feature
                 selection for figure-ground segmentation tasks, aiming
                 to maximise the segmentation performance and minimise
                 the number of selected features. The proposed methods
                 produce feature subsets that lead to solutions
                 achieving better segmentation performance with lower
                 features than those of two benchmark methods (i.e.
                 sequential forward selection and sequential backward
                 selection) and the original full feature set. This is
                 due to GP's high search ability and higher likelihood
                 of finding the global optima.

                 GP is introduced for the first time to construct high
                 level features from primitive image features, which
                 aims to improve the image segmentation performance,
                 especially on complex images. By considering
                 linear/nonlinear interactions of the original features,
                 the proposed methods construct fewer features that
                 achieve better segmentation performance than the
                 original full feature set. This investigation has shown
                 that GP is suited for figure-ground image segmentation
                 for the following reasons. Firstly, the proposed
                 methods can evolve segmenters with useful class
                 characteristic patterns to segment various types of
                 objects. Secondly, the segmentors evolved from one type
                 of foreground object can generalise well on similar
                 objects. Thirdly, both the selected and constructed
                 features of the proposed GP methods are more effective
                 than original features, with the selected/constructed
                 features being better for subsequent tasks. Finally,
                 compared with other segmentation techniques, the major
                 strengths of GP are that it does not require
                 pre-defined problem models, and can be easily adapted
                 to diverse image domains without major parameter tuning
                 or human intervention.",
}

Genetic Programming entries for Yuyu Liang

Citations