Content Based Batik Image Classification Using Wavelet Transform and Fuzzy Neural Network

Abstract

In this paper we introduce the content-based image classification using wavelet transform with Daubechies type 2 level 2 to process the characteristic texture consisting of standard deviation, mean and energy as Input variables, using the method of Fuzzy Neural Network (FNN). All the input value will be processed using fuzzyfication with 5 categories namely Very Low (VL), Low (L), Medium (M), High (H) and Very High (VH). The result will be fuzzy input in the process of classification with neural network method. Batik images will be processed using 7 (seven) types of batik motif which is ceplok, kawung, lereng, parang, megamendung, tambal and nitik. The results of the classification process using FNN is Rule generation, such that for a new image of batik motif types can be immediately determined after FNN classification is completed. For the level of precision, this method is between 90-92%, including if we use the rule generation to determine the level precision is between 90-92%.

Banten Batik Classification with Backpropagation Neural Network

“…The system successfully classified Batik Banten class from 10 different pattern. The system had better value than [5] that had accuracy [3]97.67%, [6] with accuracy value 72%, [7] which had accuracy value 99,5%, but only 2 class, [8]images classification accuracy was 86%, [9] had accuracy value 99,96% but with 5 class had been used , [4], [10]- [14] had accuracy < 92%, [15] had accuracy 80%, [16] had accuracy value 94,57%.…”

“…Some methods had been used to clssified Batik Pattern. Batik classification"s method were Support Vector Machine [2], Neural Network [3], and Decession Tree Classification [4]. The neural network had great advantages in parallel computation for classified the pattern.…”

Feature extraction methods for batik pattern recognition: A review

“…Texture feature is obtained by extracting pixel values of batik image which has undergone a greyscale transformation process and Canny Detection [20]. Feature extraction can be performed by feature extraction of wavelet transformation with Daubechies type 2 level 2 for processing the texture characteristic consisting of deviation standard, mean, and energy [21]. Geometric method for feature extraction of batik image by using the cardinal spline curve representation is divided into 2 processes for a batik image.…”

A Classification of Batik Lasem using Texture Feature Ecxtraction Based on K-Nearest Neighbor

“…The advantages of the KNN method are that it is resilient to training data that has a lot of noise and is effective when the data training is large. Whereas one of the weaknesses of the KNN is the need to determine the value of the parameter k (number of closest neighbors) [8]. Another research by [9], has been conducted by grayscale processing, binary and canny processes and the result of invariant moment's calculation and has been combinated using Canny detection to enhance result during Wavelet Transform implementation.…”