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Automatic aesthetic value assessment in photographic images

Automatic aesthetic value assessment in photographic images,10.1109/ICME.2010.5582588,Wei Jiang,Alexander C. Loui,Cathleen Daniels Cerosaletti

Automatic aesthetic value assessment in photographic images   (Citations: 1)
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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.
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    • ...4.1. Image quality evaluation There has been some recent research on characterizing consumer photographs based upon image quality as well as developing predictive algorithms [18, 19]...
    • ...In particular, the work in [19] provided an empirical study where a set of visual features that described various characteristics related to image quality and aesthetic values were used to generate multidimensional feature spaces, on top of which machine learning algorithms were developed to estimate images’ aesthetic scales...
    • ...Therefore, among the best performing features reported in [19], we use the features developed by Ke et al. in [20], including the spatial distribution of highfrequency edges, the color distribution, the hue entropy, the blur degree, the color contrast, and the brightness (6 dimensions)...

    Wei Jianget al. Automatic consumer video summarization by audio and visual analysis

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