DCT-CNN-based classification method for the Gongbi and Xieyi techniques of Chinese ink-wash paintings.

Different from the western paintings, Chinese ink-wash paintings (IWPs) have own distinctive art styles. Furthermore, Chinese IWPs can be divided into two classes, Gongbi (traditional Chinese realistic painting) and Xieyi (freehand style). The extraction of Chinese IWP features with good classification results is challenging because of similar content. This paper presents a novel framework by combining a discrete cosine transformation (DCT) and convolutional neural networks (CNNs). In this framework, a CNN automatically extracts Chinese IWP features from a small subset of the DCT coefficients of an image instead of raw pixels commonly because of its good performance. We evaluate the proposed framework on a dataset including 1400 Chinese IWPs. Experimental results show that the proposed framework achieves competitive classification performance compared to existing benchmark methods.