<![CDATA[The popularity of batik patterns in Indonesia has varied. Industrial modern devices in imaging have supported batik pattern recognition and classification. The important of product pattern information could not naturally visible. The information about batik pattern can be achieved by using the appropriate software design of image processing for extracting the features. One of the potential procedures is the unsupervised classification method based on specific feature. In this research, the specific feature extraction based on the eigenimage of batik pattern was done. In the final step, the nearest distance eigenimage between reference batik image and test batik image was used to identify the batik from the classical pattern field point of view. The results of batik image identification conformed 96.67% with the reference batik images.]]>
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