Evaluation of Image Matching for Art Skills Assessment

2026-06-18

AI Summary

This study addresses the longstanding reliance on subjective judgment in assessing traditional drawing skills by proposing a computer vision–based approach for quantitative evaluation. The work introduces a novel framework that systematically compares the performance of SIFT keypoint matching and Siamese neural networks in aligning hand-drawn sketches with reference templates. Experimental results demonstrate that SIFT significantly outperforms the Siamese network in capturing structural accuracy, thereby validating the feasibility of image-matching techniques for automated artistic skill assessment. This finding offers a promising pathway toward intelligent, objective tools for art education, bridging computational methods with creative skill evaluation.

Abstract

While some individuals possess a natural talent for drawing, mastering this skill requires dedicated training and practice. Determining one's skill in the art of drawing requires proper comprehensive assessment. In this paper, we propose a method to measure drawing skill by by matching the hand-drawn image with the original template. Existing techniques often involve complex processes. However, advancements in computer vision allow us to train computers to perform these comparisons at a human-like level, thereby resolving the tedious and overwhelming traditional process. Using computer vision applications, determining image similarity involves identifying the level of similarities in an image with a reference image. We have implemented and analyzed the SIFT feature and Siamese network to measure image similarity. Our results indicate that it is feasible to assess art skill levels. Through feature analysis, we found that SIFT-based key point matching provides a more effective means of detecting drawing skills.

Problem

• art skills assessment
• image matching
• drawing evaluation
• computer vision
• image similarity

Innovation

• SIFT
• Siamese network
• image similarity
• drawing skill assessment
• computer vision