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dc.contributor.advisor Moustafa, Mohamed
dc.contributor.author Adly, Hussein
dc.date.accessioned 2017-01-08T07:47:47Z
dc.date.created Fall 2016 en_US
dc.date.issued 2017-01-08
dc.identifier.uri http://dar.aucegypt.edu/handle/10526/4955
dc.description.abstract Texture classification is a problem that has various applications such as remote sensing and forest species recognition. Solutions tend to be custom fit to the dataset used but fails to generalize. The Convolutional Neural Network (CNN) in combination with Support Vector Machine (SVM) form a robust selection between powerful invariant feature extractor and accurate classifier. The fusion of classifiers shows the stability of classification among different datasets and slight improvement compared to state of the art methods. The classifiers are fused using confusion matrix after independent training of each using the same training set, then put to test. Statistical information about each classifier is fed to a confusion matrix that generates two confidence measures used in building two binary classifiers. The binary classifier is allowed to activate or deactivate a classifier during testing time based on a confidence measure obtained from the confusion matrix. The method obtained results approaching state of the art with a difference less than 1% in classification success rates. Moreover, the method was able to maintain this success rate among different datasets while other methods had failed to obtain similar stability. Two datasets had been used in this research Brodatz and Kylberg where the results came 98.17% and 99.70%. In comparison to conventional methods in the literature, it came as 98.9% and 99.64% respectively. en_US
dc.format.extent 62 p. en_US
dc.format.medium theses en_US
dc.language.iso en en_US
dc.rights Author retains all rights with regard to copyright. en
dc.subject classification en_US
dc.subject.lcsh Thesis (M.S.)--American University in Cairo en_US
dc.title A hybrid deep learning approach for texture analysis en_US
dc.type Text en_US
dc.subject.discipline Computer Science en_US
dc.rights.access This item is available en_US
dc.contributor.department American University in Cairo. Dept. of Computer Science and Engineering en_US
dc.description.irb American University in Cairo Institutional Review Board approval has been obtained for this item. en_US
dc.contributor.committeeMember Khalil, Mahmoud
dc.contributor.committeeMember Goneid, Amr


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  • Theses and Dissertations [1733]
    This collection includes theses and dissertations authored by American University in Cairo graduate students.

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