Prediction Model of Ancient Village Pottery Building Microspace Design Style by Integrating Machine Learning

Abstract

Microspace design styles on pottery fragments offer a captivating window into the artistic heritage and cultural practices of past societies. These intricate details and recurring patterns hold valuable clues about rituals, beliefs, and artistic expressions. However, existing approaches struggle to capture the relationships between design elements within a single piece. This limitation hinders the ability to capture the holistic meaning conveyed by the microspace design style. To overcome this limitation, this research proposes a novel approach called MoANN-DSOA for predicting microspace design styles based on ancient village pottery. MoANN-DSOA utilizes a Mosaic Attention Neural Network (MoANN) to analyze both the visual image and the relationships between design elements. This allows for a more detailed understanding of the artistic message encoded within each fragment. Additionally, a Dove Swarm Optimization Algorithm (DSOA) optimizes the MoANN architecture, potentially enhancing the accuracy of capturing intricate details. The proposed MoANN-DSOA method attains 7.78%, 27.89% and 33.335% higher accuracy and 14%, 20.81% and 32.36% higher F-Score compared to the existing methods like Categorization and Retrieval of Painted Pottery with CNNs (CNN), Utilizing CNN-VGG16-VGG19 Approach for Distinguishing Surface Treatments in Wheel-Thrown Pottery (CNN-VGG16-VGG19) and Unsupervised Feature Extraction of Ceramic Profiles using a Deep Variational Convolutional Autoencoder (DVCA) respectively. By this, the proposed MoANN-DSOA methodology paves the way for efficient information extraction from pottery image, unlocking deeper understanding and facilitating the construction of more informed historical narratives.