Abstract
We study 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. Although LDA models text documents as distributions over word topics, we model magazine covers as distributions over color-word topics. The results of our crowdsourcing experiments confirm that the model is able to successfully discover the associations between colors and linguistic concepts. Finally, we demonstrate several prototype applications that use the learned model to enable more meaningful interactions in color palette recommendation, design example retrieval, pattern recoloring, image retrieval, and image color selection.
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