Research on Intelligent Extraction of Visual Symbols and Creative Transformation Methods in the Digital Inheritance of Intangible Cultural Heritage
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Abstract
Digital inheritance of intangible cultural heritage requires efficient extraction of visual symbols while preserving morphological details and cultural integrity. Current manual tracing and shallow image-processing approaches are inefficient and may lose symbolic structure or cultural meaning during extraction and transformation. This study proposes an intelligent visual-symbol extraction and creative transformation framework based on deep feature fusion. Representative intangible cultural heritage samples, including paper-cutting, shadow puppetry, and batik, are collected and analyzed from four dimensions: morphology, color, texture, and cultural semantics. A feature database is built to support model training and transformation design. The extraction model integrates convolutional neural networks and generative adversarial networks, with non-stationary fingerprint constraints, multi-window deconvolution decoupling, overlapping masks, delay compensation, and topological consistency constraints to achieve multi-scale feature extraction, interference suppression, and precise segmentation. A Cultural Preservation Index is introduced to evaluate whether extracted symbols retain expert-verified cultural gene templates. Based on extracted symbols, a multidimensional creative transformation strategy is designed, including simplified reconstruction, meaning translation, and scene adaptation. Experiments on 800 visual-symbol samples show an extraction accuracy of 92.7%, significantly higher than single-CNN extraction, while average processing time is reduced to 0.8 s per sample. User recognition of transformed works reaches 86.2%. The framework provides an image signal processing and intelligent visual-design method for digital cultural-resource systems.
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