  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.
