A Research on Traditional Tangka Image Classification Based on Visual Features

With the development of computer vision technology, image classification has become an important research field. In order to achieve automated classification of Tang Ka images, this paper proposes a visual feature based classification method for Tang Ka images. The method first uses HOG features, Local binary patterns and color moments to extract image features, and then uses support vector machine classifier to classify images. The data in this article shows that the highest accuracy rate of the Hantangka obtained through support vector machine classification and filtering is 96.5%. The experimental results show that this method performs well in the classification of heritage thangka images and has a high classification accuracy.