<![CDATA[In this paper, an automated supervised image classification technique, specifi- cally for classifying images in the cultural heritage domain, is developed. The developed technique classifies images according to a particular date, culture, people and historical age. The proposed technique consists of two stages, fea- ture extraction using the unsupervised segmentation technique, and the classi- fication stage using supervised classification techniques. Common features are extracted, and their histograms are applied to three classifiers: k-nearest neigh- bor (KNN), logistic regression (LR), and decision tree (DT). When our tech- nique was applied to a repository of images from cultural heritage, it showed reduced complexity and improved classification accuracy. DT has achieved a higher weighted average recall. This is also represented by the weighted av- erage f-measure where DT has obtained 0.81. DT has outperformed the other classifiers in terms of classifying heritage images.]]>
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