Genetic programming evolved spatial descriptor for Indian monuments classification

Created by W.Langdon from gp-bibliography.bib Revision:1.3949

@InProceedings{Bhatt:2015:ieeeCGVIS,
  author =       "M. S. Bhatt and T. P. Patalia",
  booktitle =    "2015 IEEE International Conference on Computer
                 Graphics, Vision and Information Security (CGVIS)",
  title =        "Genetic programming evolved spatial descriptor for
                 Indian monuments classification",
  year =         "2015",
  pages =        "131--136",
  abstract =     "Travel and tourism are the largest service industries
                 in India. Every year people visit tourist places. and
                 upload pictures of their visit on social networking
                 sites or share via mobile device with friends and
                 relatives. Millions of such photographs are uploaded
                 and it is almost impossible to manually classify these
                 pictures as per the monuments they have visited.
                 Classification is helpful to hoteliers for development
                 of new hotel with state of the art amenities, to travel
                 service providers, to restaurant owners, to government
                 agencies for security etc. The proposed system extracts
                 Genetic programming evolved spatial descriptor and
                 classifies the Indian monuments visited by tourists
                 based on linear Support Vector Machine(SVM). The
                 proposed system is divided into 3 main phases:
                 preprocessing, genetic programming evolution and
                 classification. The Preprocessing phase converts images
                 into a form suitable for processing by genetic
                 programming system using Generalized Co-Occurrence
                 Matrix. The second phase generates best so far spatial
                 descriptor in the form of program based on the fitness.
                 The Fitness is calculated using SVM. Once program is
                 obtained as output it can be used for classification.
                 The proposed system is implemented in MATLAB and
                 achieves high accuracy.",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/CGVIS.2015.7449908",
  month =        nov,
  notes =        "Also known as \cite{7449908}",
}

Genetic Programming entries for M S Bhatt T P Patalia

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