Distinguishing Paintings from Photographs by Complexity Estimates



This study is aimed at exploring the ability of complexity-based metrics to distinguish between paintings and photographs. The proposed features resort to edge detection, compression and entropy estimate methods that are highly correlated with artwork complexity. Artificial neural networks based on these features were trained for this task. The relevance of various combinations of these complexity metrics is also analyzed. The results of the current study indicate that different estimates related to image complexity achieve better results than state-of-the-art feature sets based on color, texture and perceptual edges. The classification success rate achieved is 94.82% on a dataset of 5235 images.


Artificial neural networks, Complexity estimates, Edge detection, Feature extraction, Image retrieval


Neural Computing and Applications, Vol. 27, #9, pp. 1-13, Springer, December 2016


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