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

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Khan:2012:HIS,

• 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 89percent.",
• notes = "Also known as \cite{6421341}",

}

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

Citations