A Novel Reliable Approach For Image Batik Classification That Invariant With Scale And Rotation Using MU2ECS-LBP Algorithm

Pattern Recognition of Sarong Fabric Using Machine Learning Approach Based on Computer Vision for Cultural Preservation

“…The cultural heritage of each country is diverse in various forms, including historical documents [1,2], stone carving [3], text [4][5][6], and traditional fabric [7][8][9]. Indonesia has the traditional fabric recognized as one of the country's cultural heritage in the form of batik and sarong fabric.…”

“…The previous work on computer vision related to sarong fabric classification is still limited. Meanwhile, batik classification has been developed more [8,9,13]. Generally, those works involved three main processes: preprocessing, feature extraction, and classification [8,9,14].…”

“…Meanwhile, batik classification has been developed more [8,9,13]. Generally, those works involved three main processes: preprocessing, feature extraction, and classification [8,9,14]. Pre-processing was commonly performed by resizing the original image into a square form [8,9], converting the RGB image into grayscale [11], histogram equalization [15], edge detection [13], and Gaussian filter [3].…”

“…Color moments and histograms were used to generate the color features [12]. Meanwhile, GLCM [9,11], LBP [8], and wavelet transform [13] were extracted as texture features. Furthermore, shape features were generated using moment Invariant [12] and area- based [7,10].…”

“…The task of fabric classification was accomplished using machine learning. Several classifiers have been implemented, including K-Nearest Neighbor (KNN) [8,12], Decision Tree [13], Support Vector Machine (SVM) [9,14], and ANN [7,8,11].…”