Abstract
The automatic assessment of aesthetic values in consumer photographic images is an important issue for content management, organizing and retrieving images, and building digital image albums. This paper explores automatic aesthetic estimation in two different tasks: (1) to estimate fine-granularity aesthetic scores ranging from 0 to 100, a novel regression method, namely Diff-RankBoost, is proposed based on RankBoost and support vector techniques; and (2) to predict coarse-granularity aesthetic categories (e.g., visually “very pleasing” or “not pleasing”), multi-category classifiers are developed. A set of visual features describing various characteristics related to image quality and aesthetic values are used to generate multidimensional feature spaces for aesthetic estimation. Experiments over a consumer photographic image collection with user ground-truth indicate that the proposed algorithms provide promising results for automatic image aesthetic assessment.