We present a novel data-driven category-based approach to automatically assess the aesthetic appeal of photographs. In order to tackle this problem, a novel set of image segmentation methods based on feature contrast are introduced, such that luminance, sharpness, saliency, color chroma, and a measure of region relevance are computed to generate different image partitions. Image aesthetic features are computed on these regions (e.g. sharpness, colorfulness, and a novel set of light exposure features). In addition, color harmony, image simplicity, and a novel set of image composition features are measured on the overall image. Support Vector Regression models are generated for each of 7 popular image categories: animals, architecture, cityscape, floral, landscape, portraiture and seascapes. These models are analyzed to understand which features have greater influence in each of those categories, and how they perform with respect to a generic state of the art model. © 2012 Springer-Verlag.
CITATION STYLE
Obrador, P., Saad, M. A., Suryanarayan, P., & Oliver, N. (2012). Towards category-based aesthetic models of photographs. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7131 LNCS, pp. 63–76). https://doi.org/10.1007/978-3-642-27355-1_9
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