Genetic Programming for Region Detection, Feature Extraction, Feature Construction and Classification in Image Data

Created by W.Langdon from gp-bibliography.bib Revision:1.4504

  author =       "Andrew Lensen and Harith Al-Sahaf and 
                 Mengjie Zhang and Bing Xue",
  title =        "Genetic Programming for Region Detection, Feature
                 Extraction, Feature Construction and Classification in
                 Image Data",
  booktitle =    "EuroGP 2016: Proceedings of the 19th European
                 Conference on Genetic Programming",
  year =         "2016",
  month =        "30 " # mar # "--1 " # apr,
  editor =       "Malcolm I. Heywood and James McDermott and 
                 Mauro Castelli and Ernesto Costa and Kevin Sim",
  series =       "LNCS",
  volume =       "9594",
  publisher =    "Springer Verlag",
  address =      "Porto, Portugal",
  pages =        "51--67",
  organisation = "EvoStar",
  keywords =     "genetic algorithms, genetic programming, , Image
                 Classification, Feature Extraction, Feature
  isbn13 =       "978-3-319-30668-1",
  DOI =          "doi:10.1007/978-3-319-30668-1_4",
  abstract =     "Image analysis is a key area in the computer vision
                 domain that has many applications. Genetic Programming
                 (GP) has been successfully applied to this area
                 extensively, with promising results. High-level
                 features extracted from methods such as Speeded Up
                 Robust Features (SURF) and Histogram of Oriented
                 Gradients (HoG) are commonly used for object detection
                 with machine learning techniques. However, GP
                 techniques are not often used with these methods,
                 despite being applied extensively to image analysis
                 problems. Combining the training process of GP with the
                 powerful features extracted by SURF or HoG has the
                 potential to improve the performance by generating
                 high-level, domain-tailored features. This paper
                 proposes a new GP method that automatically detects
                 different regions of an image, extracts HoG features
                 from those regions, and simultaneously evolves a
                 classifier for image classification. By extending an
                 existing GP region selection approach to incorporate
                 the HoG algorithm, we present a novel way of using
                 high-level features with GP for image classification.
                 The ability of GP to explore a large search space in an
                 efficient manner allows all stages of the new method to
                 be optimised simultaneously, unlike in existing
                 approaches. The new approach is applied across a range
                 of datasets, with promising results when compared to a
                 variety of well-known machine learning techniques. Some
                 high-performing GP individuals are analysed to give
                 insight into how GP can effectively be used with
                 high-level features for image classification.

  notes =        "Part of \cite{Heywood:2016:GP} EuroGP'2016 held in
                 conjunction with EvoCOP2016, EvoMusArt2016 and

Genetic Programming entries for Andrew Lensen Harith Al-Sahaf Mengjie Zhang Bing Xue