Individual-Aware Gradient Boosting Regression for Visual Saliency Prediction of Damaged Regions in Ancient Murals

Murals are vital cultural heritage assets, yet many are increasingly threatened by long-term natural weathering, environmental erosion, and human-induced damage. Given limited conservation resources, an objective method for prioritizing restoration is urgently needed. This study proposes an Individual-Aware Gradient Boosting Regression (IA-GBR) approach to predict the visual saliency of damaged mural regions by integrating physical damage characteristics, spatial location, and observer identity. We construct an eye-tracking dataset containing complete fixation records from multiple participants viewing diverse mural damage types. IA-GBR employs a two-level feature fusion strategy that combines damage, spatial, and individual features within a gradient boosting residual learning framework. The experimental results demonstrate that IA-GBR consistently outperforms baseline methods, including linear and ridge regression, SVR, decision trees, random forests, AdaBoost, and multilayer perceptrons. Feature importance analysis further reveals the relative contributions of individual differences, damage size, spatial position, and semantic factors to saliency formation. The proposed framework provides data-driven support for restoration prioritization and advances perception-aware saliency modeling in cultural heritage conservation.