Element extraction and convolutional neural network-based classification for blue calico

Abstract

Blue calico is a highly valued folk handicraft that forms part of China’s national intangible cultural heritage. Thus, blue calico is a worthy target for reconstruction using modern image processing technology. Extracting the visual components or elements of a blue calico pattern is one way to capture the underlying design and enable innovation in traditional patterns using modern techniques. This paper presents a method of element extraction and classification based on a smart convolutional neural network (CNN), with an improved CifarNet structure, which we call CalicoNet. Initially, the algorithm for element extraction is implemented to generate element samples of blue calico. This process includes gray scaling, binarization, and contour extraction. We construct a data set of elements with 12 types. Then, four critical hyper-parameters, the batch-size, dropout ratio, learning rate, and pooling strategy, are optimized by a comparative analysis. A combination classifier strategy is subsequently added to the fully connected layers to strengthen the feature expression in the corresponding classes. Finally, the superiority of the proposed CalicoNet is verified through a comparison with other sophisticated CNNs. Experimental results demonstrate that CalicoNet achieves a validation accuracy of 99.2% for the training set, a total time of 1.13 hours for the whole data set, and a test mean accuracy precision of 98.66%. The robust performance of the proposed method across the element data set indicates that CalicoNet is a promising approach for element extraction and classification.