Abstract
Hand drawing is an indispensable professional skill in the fields of environmental design, industrial design, architectural engineering, civil engineering, and other engineering design education. Students usually imitate masterpieces to practice basic skills, which is an important link for a beginner. A system for digital management requires a function for an automatic recommendation task of different brushwork skill expressions. Thus, the classification method for brushwork is to combine hand-crafted features generated by DCNN and then use the final features for input to a tree structure classification scheme. The method improvement of the other deep learning models has effectiveness in distinguishing art ontology attributes.
Highlights
Hand painting, the purpose of which is to continue the originality of the engineering design, is the chief step of design process
Sketching or design drawing skills and techniques are essential for successfully developing the generation of environmental design (ED), industrial design (ID), architecture engineering (AE), and civil engineering (CE) [1]
SVM1 and Gray level cooccurrence matrix (GLCM) performed worse than other methods, indicating that traditional texture feature is poorer than color feature in distinguishing pen and ink brushwork
Summary
The purpose of which is to continue the originality of the engineering design, is the chief step of design process. Design drawing skills in the domain of paintings have been used for painting analysis to support applications such as brush-stroke detection [3], image recommendation, and annotation and retrieval [4,5,6,7]. These efforts include the use of handmade features (artificially designed feature extraction algorithms, which mainly extract color features, texture features, and geometric features) in the early stage to perform classification method and eventually apply deep convolutional neural network (DCNN) classification model method
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