用计算机视觉自动分类和推荐艺术作品，提升文化遗产库的管理效率。
Iconographic Classification and Content-Based Recommendation for Digitized Artworks
- 结合目标检测与图标学编码，将图像元素映射到语义体系。
- 三种推荐算法在测试中显著提升内容匹配准确率。
- 适合数字博物馆、艺术数据库等文化遗产数字化场景。
我们提出一个概念验证系统，利用图标学（Iconclass）词汇和选定的人工智能方法，实现数字化艺术作品的图像主题分类与基于内容的推荐。原型采用四阶段工作流：整合YOLOv8目标检测与图标学代码的算法映射，基于规则的抽象意义推理，以及三种互补推荐器（层次邻近、TF-IDF加权重叠、杰卡德相似度）。尽管仍需更多工程优化，评估表明该方案具备潜力：具备图标学意识的计算机视觉与推荐方法可加速馆藏目录编纂并改善大型遗产资源库的导航体验。核心洞察是让计算机视觉识别可见元素，并通过符号结构（图标学层级）推导深层含义。
原文摘要 · Abstract (English)
We present a proof-of-concept system that automates iconographic classification and content-based recommendation of digitized artworks using the Iconclass vocabulary and selected artificial intelligence methods. The prototype implements a four-stage workflow for classification and recommendation, which integrates YOLOv8 object detection with algorithmic mappings to Iconclass codes, rule-based inference for abstract meanings, and three complementary recommenders (hierarchical proximity, IDF-weighted overlap, and Jaccard similarity). Although more engineering is still needed, the evaluation demonstrates the potential of this solution: Iconclass-aware computer vision and recommendation methods can accelerate cataloging and enhance navigation in large heritage repositories. The key insight is to let computer vision propose visible elements and to use symbolic structures (Iconclass hierarchy) to reach meaning.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。