Cracked Wall Image Classification Based on Deep Neural Network Using Visibility Graph Features

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dc.contributor.authorAltundogan T.G.
dc.contributor.authorKarakose M.
dc.date.accessioned2024-07-22T08:05:36Z
dc.date.available2024-07-22T08:05:36Z
dc.date.issued2021
dc.description.abstractVisibility graphs are graphs created by making use of the relations of objects with each other depending on their visibility features. Today, visibility graphs are used quite frequently in signal processing applications. In this study, cracked and non-cracked wall images taken from a dataset were classified by a deep neural network depending on the visibility graph properties. In the proposed method, firstly, histograms of the images are obtained. The resulting histogram is then expressed by visibility graphs. A feature vector of each image is created with the maximum clique and maximum degree features of the obtained visibility graphs. Then, deep neural network training is performed with the feature vectors created. The classification success of the proposed method on images separated for testing is 99%. © 2021 IEEE.
dc.identifier.DOI-ID10.1109/3ICT53449.2021.9581830
dc.identifier.urihttp://akademikarsiv.cbu.edu.tr:4000/handle/123456789/13186
dc.language.isoEnglish
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.subjectDeep neural networks
dc.subjectGraphic methods
dc.subjectImage classification
dc.subjectVisibility
dc.subjectClassifieds
dc.subjectFeatures vector
dc.subjectGraph features
dc.subjectGraph properties
dc.subjectImages classification
dc.subjectImages processing
dc.subjectMaximum clique
dc.subjectMaximum degree
dc.subjectSignal processing applications
dc.subjectVisibility graphs
dc.subjectCrack detection
dc.titleCracked Wall Image Classification Based on Deep Neural Network Using Visibility Graph Features
dc.typeConference paper

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