Colors $-$Messengers of Concepts: Visual Design Mining for Learning Color Semantics
This work addresses the problem of understanding color semantics for designers and researchers in visual design, but it is incremental as it applies an existing method to a new domain.
The paper tackled the problem of learning color semantics by modeling magazine cover designs using an extended LDA topic model, and the results showed that the model successfully discovered associations between colors and concepts, as confirmed by crowdsourced experiments.
This paper studies the concept of color semantics by modeling a dataset of magazine cover designs, evaluating the model via crowdsourcing, and demonstrating several prototypes that facilitate color-related design tasks. We investigate a probabilistic generative modeling framework that expresses semantic concepts as a combination of color and word distributions $-$color-word topics. We adopt an extension to Latent Dirichlet Allocation (LDA) topic modeling called LDA-dual to infer a set of color-word topics over a corpus of 2,654 magazine covers spanning 71 distinct titles and 12 genres. While LDA models text documents as distributions over word topics, we model magazine covers as distributions over color-word topics. The results of our crowdsourced experiments confirm that the model is able to successfully discover the associations between colors and linguistic concepts. Finally, we demonstrate several simple prototypes that apply the learned model to color palette recommendation, design example retrieval, image retrieval, image color selection, and image recoloring.