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Alex Leykin, Florin Cutzu; Distinguishing paintings from photographs. Journal of Vision 2002;2(7):214. doi: 10.1167/2.7.214.
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© ARVO (1962-2015); The Authors (2016-present)
We present a computational method for automatically distinguishing photographs from paintings. Our approach is based on two types of features: image color and image shape characteristics.
The use of color in this classification task is based on the observation that while intensity edges tend to coincide with color edges in photographs, there are significantly more color edges than intensity edges in paintings.
The second difference between photographs and pictures is at the level of image shape detail, as reflected in the intensity edge structure and texture properties. Intuitively, the bio-mechanical characteristics of the fine hand movements involved in producing the small-scale shape details result in paintings having quite different statistical characteristics than photographs of natural scenes. These characteristics were quantified using a wavelet transforms, and the classification proper was obtained by training a neural network.
The results indicate that whereas each criterion in isolation is a rather weak classifier (65–70% correct) , a conjunction of several weak criteria yields good classification performance.
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