Object Categorization Using Genetic Programming

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

@InProceedings{Korra:2010:ICGEC,

• author = "Jyothi Korra and Susheela Devi",
• title = "Object Categorization Using Genetic Programming",
• booktitle = "Fourth International Conference on Genetic and Evolutionary Computing (ICGEC 2010)",
• year = "2010",
• month = "13-15 " # dec,
• pages = "297--300",
• address = "Shenzhen, China",
• abstract = "In Computer Vision, problem of identifying or classifying the objects present in an image is called Object Categorisation. It is challenging problem, especially when the images have clutter background, occlusions or different lighting conditions. Many vision features have been proposed which aid object categorization even in such adverse conditions. Past research has shown that, employing multiple features rather than any single features leads to better recognition. Multiple Kernel Learning (MKL) framework has been developed for learning an optimal combination of features for object categorization. Existing MKL methods use linear combination of base kernels which may not be optimal for object categorization. Real-world object categorisation may need to consider complex combination of kernels(non-linear) and not only linear combination. Evolving non-linear functions of base kernels using Genetic Programming is proposed in this paper. Experiment results show that non-kernel generated using genetic programming gives good accuracy as compared to linear combination of kernels.",
• keywords = "genetic algorithms, genetic programming, SVM, MKL method, computer vision, linear combination, linear combination, multiple kernel learning framework, real world object categorisation, vision feature, computer vision, image classification, object detection, support vector machines",
• DOI = "doi:10.1109/ICGEC.2010.80",
• notes = "Caltech-5 Also known as \cite{5715428}",

}

Genetic Programming entries for Jyothi Korra V Susheela Devi

Citations