A hybrid Genetic Programming approach to feature detection and image classification

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

@InProceedings{Lensen:2015:IVCNZ,
  author =       "Andrew Lensen and Harith Al-Sahaf and 
                 Mengjie Zhang and Bing Xue",
  booktitle =    "2015 International Conference on Image and Vision
                 Computing New Zealand (IVCNZ)",
  title =        "A hybrid Genetic Programming approach to feature
                 detection and image classification",
  year =         "2015",
  abstract =     "Image classification is a crucial task in Computer
                 Vision. Feature detection represents a key component of
                 the image classification process, which aims at
                 detecting a set of important features that have the
                 potential to facilitate the classification task. In
                 this paper, we propose a Genetic Programming (GP)
                 approach to image feature detection. The proposed
                 method uses the Speeded Up Robust Features (SURF)
                 method to extract features from regions automatically
                 selected by GP, and adopts a wrapper approach combined
                 with a voting scheme to perform image classification.
                 The proposed approach is evaluated using three datasets
                 of increasing difficulty, and is compared to five
                 popularly used machine learning methods: Support Vector
                 Machines, Random Forest, Naive Bayes, Decision Trees,
                 and Adaptive Boosting. The experimental results show
                 the proposed approach has achieved comparable or better
                 performance than the five existing methods on all three
                 datasets, and reveal its capability to automatically
                 detect good regions from a large image from which good
                 features are automatically constructed.",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/IVCNZ.2015.7761564",
  month =        nov,
  notes =        "Also known as \cite{7761564}",
}

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

Citations