Classification of Camouflage Images Using Local Binary Patterns (LBP)

Hidden objects in camouflage images have almost the same texture, color and pattern features as the background image they are in. Since the camouflaged object shows almost identical texture features with the background, it is a very difficult problem to detect and classify. In this study, the textural features of all the images in the data set were extracted by using local binary pattern (LBP) on camouflage images taken from an available data set. The system was trained according to these extracted features and the learning process was carried out. Artificial Neural Networks (ANN), K-Nearest Neighborhood Algorithm (KNN) and Support Vector Machines (SVM) were used for the classification process after the learning process. As a result of experimental studies, the best result was obtained with 92% success with LBP and YSA method. Classification success rate of 89% was obtained when LBP and SVM were used. When LBP and KNN were used, a classification success rate of 87.77% was obtained.