Creating an intelligent search and retrieval system for artwork images, particularly paintings, is crucial for documenting cultural heritage, fostering wider public engagement, and advancing artistic analysis and interpretation. Visual-Semantic Embedding (VSE) networks are deep learning models used for information retrieval, which learn joint representations of textual and visual data, enabling 1) cross-modal search and retrieval tasks, such as image-to-text and text-to-image retrieval; and 2) relation-focused retrieval to capture entity relationships and provide more contextually relevant search results. Although VSE networks have played a significant role in cross-modal information retrieval, their application to painting datasets, such as ArtUK, remains unexplored. This paper introduces BoonArt, a VSE-based cross-modal search engine that allows users to search for images using textual queries, and to obtain textual descriptions along with the corresponding images when using image queries. The performance of BoonArt was evaluated using the ArtUK dataset. Experimental evaluations revealed that BoonArt achieved 97% Recall@10 for image-to-text retrieval, and 97.4% Recall@10 for text-to-image Retrieval. By bridging the gap between textual and visual modalities, BoonArt provides a much-improved search performance compared to traditional search engines, such as the one provided by the ArtUK website. BoonArt can be utilised to work with other artwork datasets.

翻译：构建面向艺术品图像（尤其是绘画作品）的智能搜索与检索系统，对于文化遗产数字化记录、促进公众广泛参与以及深化艺术分析与阐释具有重要意义。视觉语义嵌入网络（VSE）是一类用于信息检索的深度学习模型，通过学习文本与视觉数据的联合表征，能够实现：1）跨模态搜索与检索任务（如图像到文本与文本到图像的检索）；2）聚焦关系的检索，可捕捉实体间关联并提供更具语境相关性的检索结果。尽管VSE网络在跨模态信息检索领域发挥了重要作用，但其在ArtUK等绘画数据集上的应用仍属空白。本文提出BoonArt——一种基于VSE的跨模态搜索引擎，允许用户通过文本查询搜索图像，亦可在使用图像查询时获取对应的文本描述及图像。基于ArtUK数据集的实验评估表明，BoonArt在图像到文本检索任务中达到97%的Recall@10，在文本到图像检索任务中达到97.4%的Recall@10。通过弥合文本与视觉模态之间的鸿沟，BoonArt相较于ArtUK网站等传统搜索引擎，显著提升了搜索性能。该框架可适配应用于其他艺术品数据集。