  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.
