In this research will be done the selection of features to the combination feature of texture and feature of shape from batik motifs. The feature combination causes enhancement in the number of features causing dataset size changes in the classification process.
#Batik mega mendung vector definition how to
The problem in this research is how to get the potential feature to classified the motif of batik. Features can be utilized separately or combined between features. Classification of batik motif has been done by using various features such as texture features, shape features, and color features. All rights reserved.įeatures in the classification process have an important role. © 2018 Institute of Advanced Engineering and Science. This research produces a model of motif classification on the Bomba textile which has the classification accuracy of 94.6% and error rate of 5.4%. The use of a single of texture features by involving all features at all angles can improve the accuracy of the classification model. This research will implement Quadratic Vector Machine (QSVM) method with texture feature on Bomba textile pattern. Texture features obtained from Gray-Level Co-occurrence matrices (GLCM) method consisting of energy, contrast, homogenity and correlation with four angles 0°, 45°, 90°, and 135°. The features used to classify the Bomba textile motif is the textural feature. Data classification is needed to recognize the motif of each Bomba textile pattern and to cluster it into the appropriate class. The problem in this research is the difficulty in classifying every The Bomba textile motif in each class. Bomba Textile has many motif patterns and varied colors. Bomba Textile has a unique pattern and has a philosophical meaning in human life in Sulawesi Tengah.
The Bomba textile is one of the textile fabrics in Indonesia used in a province called Sulawesi Tengah. The system performs the easy-to-use application for the users in which they may easily search the Batik images with involving both the color and shape features. For experimental study, we apply our proposed system to 210 Batik image dataset from 3 common Batik kinds of pattern: Kawung, Parang and Mega Mendung. Finally, the distribution of color moments are used to aggregate the extracted color and shape features. The extracted shape features from the Hu's moment consists of orthogonal moment invariants those can be used for scale, position, and rotation invariant pattern identification for the Batik patterns. Beside the color features, we also use shape features of the Batik by applying Hu's moment. It can uniformly represent the distribution of image colors and reduce complexity of the colors. We use 3D-Vector Quantization for color feature extraction. In this paper we propose a new system for Batik image search with providing an analytical function for feature extraction by involving color and shape features and combining the extracted features. Due to many diverse Batik' patterns and colors, it is difficult to retrive the Batik images from both the color and pattern. Each region in Indonesian has specific colors and shapes reflecting the identity of the region. Batik is a culture-dependent technique and symbolism surrounding hand-dyed cotton and silk garments from Indonesia It an cultural art that has a long history of acculturation, with diverse patterns influenced by a variety of cultures.
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