Abstract
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 this https URL to try our codes.
Abstract (translated)
在分析大量数字存储的历史图像数据时，现有的基于内容的检索方法通常忽视了重要的非语义信息，从而限制了它们在多主题灵活探索中的有效性。为了拓宽图像检索方法的应用范围，并揭示更一般的模式，我们创新地将计算美学的关键因素——图像构图——引入了这一主题。通过明确地将CNN提取的构图信息与设计检索模型集成，我们的方法同时考虑了图像的构图规则和语义信息。定性和定量的实验证明，引导构图信息的图像检索网络比仅依赖内容信息的网络在接近目标图像的人感知数据库中表现更好，从而有助于在人类感知中更准确地识别出图像。请访问此链接尝试我们的代码。
URL
https://arxiv.org/abs/2403.14287
