Ancient Porcelain Shards Classifications Based on Color Features

In this paper we propose an improve algorithm for ancient porcelain classification, which contain three steps. First, image preprocessing. A color quantization method in HSI color space is performed to generate gray image. Second, feature extraction. An approach of color-texture features extraction is proposed based on Gabor filter, which is only depended on frequency. In order to reduce number of features, principal component analysis is adopted. Last, porcelain shards classifications. Nearest Neighbor Method is adopted to classify shards. An ancient shards classification prototype system is developed and help archeologist easily to do research on porcelain and restore the broken porcelain. The system has been practical to the recovery of Yao Zhou´s porcelains, which are famous in ancient China.