Presentation Type
Poster
Start Date
5-7-2014 11:00 AM
End Date
5-7-2014 1:00 PM
Subjects
Composition (Photography) -- Analysis, Photographic images -- Perception -- Evaluation, Photographic images -- Classification
Abstract
Rule of Balance is one of the most important composition rules in photography, which can be used as a standard for photo quality assessment. The rule of balance states that images with evenly distributed visual elements are visually pleasing and thus are highly aesthetic. This work presents a method to automatically classify balanced and unbalanced images. Detecting the rule of balance requires a robust technique to locate and analyze important objects and visual elements, which involves understanding of the image content. Since semantic understanding is currently beyond the state of the art in computer vision, we employ the saliency maps as an alternative. We design a range of features according to the definition and effects of the rule of balance. Our experiments with a variety of machine learning techniques ([8-11]) and saliency analysis methods ([2-6]) demonstrate an encouraging performance in detecting vertical and horizontal balanced images. For future works, the balance detecting system can be developed into a subroutine for an automatic evaluation of professional photography.
Rights
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Persistent Identifier
http://archives.pdx.edu/ds/psu/11450
Detecting Rule of Balance in Photography
Rule of Balance is one of the most important composition rules in photography, which can be used as a standard for photo quality assessment. The rule of balance states that images with evenly distributed visual elements are visually pleasing and thus are highly aesthetic. This work presents a method to automatically classify balanced and unbalanced images. Detecting the rule of balance requires a robust technique to locate and analyze important objects and visual elements, which involves understanding of the image content. Since semantic understanding is currently beyond the state of the art in computer vision, we employ the saliency maps as an alternative. We design a range of features according to the definition and effects of the rule of balance. Our experiments with a variety of machine learning techniques ([8-11]) and saliency analysis methods ([2-6]) demonstrate an encouraging performance in detecting vertical and horizontal balanced images. For future works, the balance detecting system can be developed into a subroutine for an automatic evaluation of professional photography.