TOWARDS CLASSIFYING CLASSICAL BATIK IMAGES - Unpar

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.
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