Evolving novel image features using genetic programming-based image transforms
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Image classification for content-based indexing
IEEE Transactions on Image Processing
Feature selection and novelty in computational aesthetics
EvoMUSART'13 Proceedings of the Second international conference on Evolutionary and Biologically Inspired Music, Sound, Art and Design
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We explore a new definition of creativity -- one which emphasizes the statistical capacity of a system to generate previously unseen patterns -- and discuss motivations for this perspective in the context of machine learning. We show the definition to be computationally tractable, and apply it to the domain of generative art, utilizing a collection of features drawn from image processing. We next utilize our model of creativity in an interactive evolutionary art task, that of generating biomorphs. An individual biomorph is considered a potentially creative system by considering its capacity to generate novel children. We consider the creativity of biomorphs discovered via interactive evolution, via our creativity measure, and as a control, via totally random generation. It is shown that both the former methods find individuals deemed creative by our measure; Further, we argue that several of the discovered "creative" individuals are novel in a human-understandable way. We conclude that our creativity measure has the capacity to aid in user-guided evolutionary tasks.