Visual category recognition for the improved storage and retrieval performance of the CCTV camera system

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

  author =       "Asif Ali Khan and Syed Faiz Akbar Shah and 
                 Fahad Ullah and Nasru Minallah",
  booktitle =    "12th International Conference on Hybrid Intelligent
                 Systems (HIS 2012)",
  title =        "Visual category recognition for the improved storage
                 and retrieval performance of the CCTV camera system",
  year =         "2012",
  pages =        "241--246",
  keywords =     "genetic algorithms, genetic programming, closed
                 circuit television, image retrieval, image sequences,
                 object recognition, statistical analysis, support
                 vector machines, transforms, CCTV camera system, CGP,
                 Caltech 101 dataset, Cartesian genetic programming
                 algorithms, KNN, LDA, SIFT, SVM, category level object
                 recognition system, image sequences, improved storage
                 performance, k-nearest neighbours, linear discriminant
                 analysis, retrieval performance, scale invariant
                 feature transform, statistical algorithms, support
                 vector machine, visual category recognition, Accuracy,
                 Cameras, Feature extraction, Support vector machines,
                 Testing, Training, Cartesian Genetic programming,
                 Category Recognition, Feature Extraction, K-Nearest
                 Neighbours, Linear Discriminant Analysis, Scale
                 Invariant Feature Transform, Support Vector Machine",
  DOI =          "doi:10.1109/HIS.2012.6421341",
  size =         "6 pages",
  abstract =     "In this paper, we propose a category level object
                 recognition system for the efficient use of CCTV
                 cameras in terms of storage and retrieval. We
                 investigate the performance of the proposed approach by
                 using four different classifiers. More specifically, we
                 considered image sequences with cars, bikes and
                 pedestrian as our three targeted object categories for
                 classification and ultimately efficient storage and
                 retrieval with reference to our CCTV cameras system. We
                 used Linear Discriminant Analysis (LDA), Support Vector
                 Machine (SVM), K-Nearest Neighbours (KNN) and Cartesian
                 Genetic Programming (CGP) algorithms for the considered
                 object categories classification. The Linear
                 Discriminant Analysis (LDA), KNN and Support Vector
                 Machine (SVM) are Statistical algorithms while
                 Cartesian Genetic Programming (CGP) is Evolutionary
                 Algorithm. More specifically, we used the standard
                 Caltech 101 dataset for investigating the performance
                 of our proposed classifiers. Scale Invariant Feature
                 Transform (SIFT) has been used to extract the scale,
                 orientation and translational invariant features from
                 the considered images which are input to the
                 classifiers. Our empirical results show that in most of
                 the cases, the results of LDA and SVM are relatively
                 the same. To be specific, LDA gives an average accuracy
                 of 85.3percent and SVM 83.6percent. Similarly, KNN
                 gives an average accuracy of 74.6percent while CGP
                 outperforming the three gives accuracy rate of
  notes =        "Also known as \cite{6421341}",

Genetic Programming entries for Asif Ali Khan Syed Faiz Akbar Shah Fahad Ullah Nasru Minallah