TOWARDS CLASSIFYING CLASSICAL BATIK IMAGES - Unpar

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Training the back-propagation neural networks. To train the neural network (see Figure 3) and compute the proper weights, a set of images that are visually similar (positive examples) and a set of images that are not similar (negative examples) are provided. The input for the training is the similarity values of the images computed from texture and shape feature matching. x1 x2 input layer V21 V11 V22 V12 hidden layer Figure 3. Back-propagation neural networks. Experiment At this early stage of research, experiments have been conducted to find the proper technique of obtaining image edges and to prove visually that shapes and textures should be used in classifying images. By observing a sample of batik images, it is found that many of them have unsharp motif edges but sharp edges of isen-isen or other additional ornaments that have no meaning. Applying Canny edge detector to such images directly would generate edges that are inappropriate for classifying. Figure 4 shows banji batik edges where swastika shape, which is needed for classifying is hidden among other unnecessary ornaments. Figure 4. Banji batik edges shape without image enhacement. To deal with this problem, images are enhanced. First, the intensity of the shapes having low value of intensity is increased. In this experiment, intensity value of [0 0.5] is transformed to [0.5 1]. Second, the image is sharpened by stretching intensity value to a certain range, in this experiment, it is [0, 1]. Canny detector is then applied to the enhanced images. Figure 5 shows some edges generated from images shown on Figure 1. It is shown on Figure 5.a, that swastika 6 y12 y22 output layer o
appears clearly. By enhancing the images and using a certain threshold value (in this experiment, it is 0.5) to Canny detector, isen-isen is sometime eliminated from the edges giving better shapes. a.banji b. ceplok c. lung-lungan d. semen Figure 5. Batik edges detected by Canny detector. It also is found that the output of Canny detector applied to batik images having fine motif is fine and dense edges (see Fig. 6). Applying the methods mentioned previously to generate shape features from these edges would potentially give bad or erroneous features. Generating texture feature from this kind of image is more appropriate. Figure 6. An example of Canny output having fine and dense edges. Conclusion Image preprocessing of classical batik images plays an important role in generating the appropriate features that could lead to high accuracy result of the classification. As there are many batik images containing large and small shapes of ornaments, generating feature for both, in the form of shape and texture feature, would be needed by the classification technique in order to increase the accuracy. Further work is needed to prove that the classification technique proposed performs well. The work would include implementing the system and conduct experiments, with the aim of observing the performance of the technique, using large number of images representing real world batik images. 7
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TOWARDS CLASSIFYING CLASSICAL BATIK IMAGES - Unpar

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