Comparison of Methods for Batik Classification Using Multi Texton Histogram

According to the result, MTH as feature extraction, k-NN or SVM as a classifier can be applied on Batik image classification. According to the previous sentences, this study attempts to develop Batik classification using MTH as feature extraction method. Table 1 illustrates the cross-validation experiment results using sixfolding. 4.5Experiment scenario of equal distribution 50/50 In this experiment, 300 Batik images are grouped into equal number, 50% as training data and 50% as testing data. [...]three images in each class are employed as training data, and three other images in each class are used as testing data. Table 2 shows the experiment results of equal distribution scenario. 4.6Experiment scenario of 60/40 distribution In this experiment, 300 Batik images are divided into 60% as training data and 40% as testing data. [...]four images in each class are used as training data, and two other images in each class are used as testing data. Table 3 shows the experiment results of 60/40 distribution scenario. 4.7Experiment scenario of 70/30 distribution In this experiment, 300 Batik images are grouped into 70% as training data and 30% as testing data. [...]five images in each class are used as training data, and one image in each class is used as testing data.