Repeated pattern extraction with knowledge-based attention and semantic embeddings

Repeat pattern is a common structure in textile or graphic design, while scalable and reusable designs are required for production. We propose a new approach, based on Convolutional Neural Network (CNN), for automatic extraction of repeat patterns in this paper. CNNs are good at detecting repeat patterns through extracting multilevel features in the convolutional layers. In our method, we select filters quickly in single images by leveraging the combination of the learned filters from AlexNet's convolutional layers and boundary detection results from VGG16. Moreover, we use template matching to optimize final outputs in order to improve precision. We composed a dataset with 30 images, covering stripe, check and dot distributed patterns, each image with manual ground truth labels. The experimental results show that our method outperforms the state-of-the-art method in both precision and running speeds.