Discovery of Texture Features Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Brian T. K. Lam",
  title =        "Discovery of Texture Features Using Genetic
  school =       "School of Computer Science and Information Technology,
                 RMIT University",
  year =         "2012",
  address =      "Melbourne, Victoria, Australia",
  month =        "30 " # mar,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "",
  URL =          "",
  size =         "191 pages",
  abstract =     "A visual texture is an image in which a basic pattern
                 or texture element is repeated many times, for example
                 grass in a lawn or bricks in a wall. Within each
                 texture element, the grey levels and their positions
                 are arranged in a sufficiently similar manner so that
                 the patterns take on a uniform appearance. The process
                 of characterising the underlying relationships within
                 texture elements and their placement can be considered
                 as a form of feature extraction. These relationships
                 allow salient features of different textures to be used
                 in texture classification, segmentation and synthesis
                 tasks. Texture classification is an important task in
                 areas such as remote sensing, surface inspection,
                 medical imaging and content retrieval from image

                 Most texture feature extraction methods are derived
                 from human intuition after much contemplation. Texture
                 feature extraction remains a challenging problem due to
                 the diversity and complexity of natural textures. In
                 this thesis we investigate the evolution of feature
                 extraction programs using tree based genetic

                 Our main hypothesis is that given the right fitness
                 evaluation, it may be possible to generate new feature
                 extraction programs independent of human intuition from
                 basic properties of images such as pixel intensities,
                 histograms and pixel positions. We used tree based
                 genetic programming and a learning set of thirteen
                 Brodatz textures to evolve feature extraction programs.
                 We have investigated three kinds of inputs/terminals:
                 raw pixels, histograms and a spatial encoding. The
                 function set consisted of +,- to facilitate the
                 analysis of the evolved programs. Fitness is computed
                 with a novel application of clustering. A program in
                 the population is applied to a selection of images of
                 two textures in the learning set. If the program
                 delivers widely separated clusters for the two
                 textures, it is considered to be very fit.",
  abstract =     "The evolved programs were then used on a different
                 training set of images to get a nearest neighbour
                 classifier which is evaluated against a testing set. We
                 have used the evolved feature extraction programs in 4
                 different classification tasks: (1) a thirteen class
                 problem involving the same textures as in the learning
                 set, but with an independent training and test set; (2)
                 a four class problem comprising Brodatz textures not in
                 the learning set; (3) a fifteen class problem
                 comprising Vistex textures; and (4) a three class
                 problem of malt classification.

                 The evolved programs were evaluated by classification
                 accuracy on the testing sets. Raw pixel input gave a
                 classification accuracy of 50percent for task 1 and
                 45percent for task 3. Histogram input gave a
                 classification accuracy of 81percent and 75percent for
                 these tasks while the spatial encoding gave accuracies
                 of 75percent and 61percent. The histogram
                 representation was found to be the most effective
                 representation. The evolved programs were compared with
                 18 human derived methods on tasks 1 and 3. The accuracy
                 of the evolved programs was ranked 14 out of 19 for
                 task 1 and 9 out of 19 for task 3. Task 2 was only
                 performed using histogram inputs and the accuracy was
                 100percent compared with 95percent for the grey level
                 co-occurrence method. These results indicate that, on
                 these tasks, the evolved feature extraction programs
                 are competitive with human derived methods.

                 Task 4, malt classification, is a difficult real world
                 problem. We used the best performing input, histograms,
                 for this task. We obtained a classification accuracy of
                 67percent which is better than the Gabor and Haar
                 methods but worse than the gray level co-occurrence
                 matrix and the grey level run length methods. However,
                 when we combined the evolved features with human
                 derived features, we improved the classification
                 accuracy by 15percent. This suggests that the evolved
                 features have captured texture regularities not
                 captured in the human derived methods. The contribution
                 of the evolved features towards the improved accuracy
                 was confirmed when the combined evolved and human
                 derived feature set was subjected to feature selection.
                 There was a high percentage of evolved features among
                 the selected features.

                 The value of our approach lies in the fact that feature
                 extraction programs can be evolved from simple inputs
                 such as histograms and arithmetic operations without
                 much domain knowledge. From a practitioner point of
                 view, our set of programs has the advantage of not
                 requiring the user to set the parameter values as
                 required by many human derived methods. For
                 researchers, our approach shows that it is possible to
                 evolve, from simple inputs, feature extraction programs
                 that can perform as well as those derived by human
  notes =        "Supervisor: Victor Ciesielski",

Genetic Programming entries for Brian Lam