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Image compositions are helpful in the study of image structures and assist indiscovering the semantics of the underlying scene portrayed across art formsand styles. With the digitization of artworks in recent years, thousands ofimages of a particular scene or narrative could potentially be linked together.However, manually linking this data with consistent objectiveness can be ahighly challenging and time-consuming task. In this work, we present a novelapproach called Image Composition Canvas (ICC++) to compare and retrieve imageshaving similar compositional elements. ICC++ is an improvement over ICCspecializing in generating low and high-level features (compositional elements)motivated by Max Imdahl's work. To this end, we present a rigorous quantitativeand qualitative comparison of our approach with traditional andstate-of-the-art (SOTA) methods showing that our proposed method outperformsall of them. In combination with deep features, our method outperforms the bestdeep learning-based method, opening the research direction for explainablemachine learning for digital humanities. We will release the code and the datapost-publication.

ICC++: Explainable Image Retrieval for Art Historical Corpora using Image Composition Canvas