Toward automatic and flexible concept transfer

This paper introduces a novel approach to automatic, yet flexible, image concept transfer; examples of concepts are “romantic”, “earthy”, and “luscious”. The presented method modifies the color content of an input image given only a concept specified by a user in natural language, thereby requiring minimal user input. This method is particularly useful for users who are aware of the message they wish to convey in the transferred image while being unsure of the color combination needed to achieve the corresponding transfer. Our framework is flexible for two reasons. First, the user may select one of two modalities to map input image chromaticities to target concept chromaticities depending on the level of photo-realism required. Second, the user may adjust the intensity level of the concept transfer to his/her liking with a single parameter. The proposed method uses a convex clustering algorithm, with a novel pruning mechanism, to automatically set the complexity of models of chromatic content. Results show that our approach yields transferred images which effectively represent concepts as confirmed by a user study.

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► We introduce an automatic yet flexible framework for transferring moods or “concepts” to images.
► The model can produce results with varying degrees of photo-realism, according to the tastes of the user.
► The framework includes a novel method to set the complexity of the color model of an image.