Image Feature Description by Frequent Patterns

The classification of image data becomes important, due to the increasing application of digital images, unsupervised classification technology with high capacity is necessary for processing digital images. In this paper, we propose an unsupervised approach of image pattern description and classification. In order to collect frequently appeared patterns in images, a compressibility feature space is built in an unsupervised manner. Based on this feature space the proposed approach transforms images to sequences, which are then divided into segments and replaced by characters. Finally, the similarities among compressibility vectors of texts are used for classification, instead of using texts themselves. Our experiments showed that the proposed approach is effective.