In analyzing vast amounts of digitally stored historical image data, existing content-based retrieval methods often overlook significant non-semantic information, limiting their effectiveness for flexible exploration across varied themes. To broaden the applicability of image retrieval methods for diverse purposes and uncover more general patterns, we innovatively introduce a crucial factor from computational aesthetics, namely image composition, into this topic. By explicitly integrating composition-related information extracted by CNN into the designed retrieval model, our method considers both the image's composition rules and semantic information. Qualitative and quantitative experiments demonstrate that the image retrieval network guided by composition information outperforms those relying solely on content information, facilitating the identification of images in databases closer to the target image in human perception. Please visit https://github.com/linty5/CCBIR to try our codes.

翻译：在分析大量数字化存储的历史图像数据时，现有基于内容的检索方法往往忽略了重要的非语义信息，限制了其在跨主题灵活探索中的有效性。为了拓展图像检索方法在多样化场景中的适用性并揭示更通用的模式，我们创新性地引入了计算美学中的关键因素——图像构图，将其融入该主题。通过将CNN提取的构图相关信息显式集成到所设计的检索模型中，我们的方法同时考虑了图像的构图规则与语义信息。定性与定量实验表明，受构图信息引导的图像检索网络优于仅依赖内容信息的方法，有助于从数据库中识别出更符合人类感知中与目标图像接近的图像。请访问https://github.com/linty5/CCBIR 试用我们的代码。