Cracked Wall Image Classification Based on Deep Neural Network Using Visibility Graph Features - Altundogan, Turan Goktug; Karakose, Mehmet (Institute of Electrical and Electronics Engineers Inc., 2021)
Visibility 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.
2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2021 -- 29 September 2021 through 30 September 2021 -- Virtual, Online -- 173514
Keywords: Crack Detection; Image Processing; Visibility Graphs