Automated mold defects classification in paintings: A comparison of machine learning and rule-based techniques.

ABSTRACT: Mold defects pose a significant risk to the preservation of valuable fine art paintings, typically arising from fungal growth in humid environments. This paper presents a novel approach for detecting and categorizing mold defects in fine art paintings. The technique leverages a feature extraction method called Derivative Level Thresholding to pinpoint suspicious regions within an image. Subsequently, these regions are classified as mold defects using either morphological filtering or machine learning models such as Classification and Regression Trees (CART) and Linear Discriminant Analysis (LDA). The efficacy of these methods was evaluated using the Mold Features Dataset (MFD) and a separate set of test images. Results indicate that both methods improve the accuracy and precision of mold d

SUBMITTER: Nordin H 

PROVIDER: S-EPMC11761149 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature