Traditional Patterns Segmentation Algorithm Based on Memory Learning Model

The rich and enduring traditional culture cultivated by the Chinese nation over millennia serves as the core and essence of the Chinese heritage. In this intricate tapestry of culture, traditional patterns hold a significant and revered position. These patterns not only reflect the aesthetic values and artistic achievements of ancient Chinese civilization but also carry deep cultural and historical significance. However, the segmentation and recognition of traditional patterns pose significant challenges due to the limited availability of annotated data, the complexity of pattern variations, and the interference of material textures. To address these challenges, this article proposes a traditional pattern segmentation algorithm based on the memory learning model. The memory learning model, as the guiding principle of the algorithm, leverages prior knowledge from related domains to enable the algorithm to generalize effectively with limited annotated data. The algorithm consists of two key components: a saliency prior module and a multi-scale feature matching module. The saliency prior module uses phase spectrum information to generate saliency maps, guiding the model to focus on high-frequency information such as edges and contours. The multi-scale feature matching module captures features at different scales, improving the robustness and accuracy of the segmentation. We construct a traditional pattern dataset by introducing a phase spectrum–amplitude spectrum fusion algorithm, which enhances the model’s ability to focus on phase consistency information. Experimental results on a traditional pattern dataset show that our proposed algorithm outperforms state-of-the-art methods, demonstrating its superior performance and robustness in handling complex and diverse pattern segmentation tasks.