Estimating the photorealism of images: Distinguishing paintings from photographs

Automatic classification of an image as a photograph of a real-scene or as a painting is potentially useful for image retrieval and website filtering applications. The main contribution of this paper is the proposition of several features derived from the color, edge, and gray-scale-texture information of the image that effectively discriminate paintings from photographs. For example, we found that paintings contain significantly more pure-color edges, and that certain gray-scale-texture measurements (mean and variance of Gabor filters) are larger for photographs. Using a large set of images (???????? ) collected from different web sites, the proposed features exhibit very promising classification performance (over ? ). A comparative analysis of the automatic classification results and psychophysical data is reported, suggesting that the proposed automatic classifier estimates the perceptual photorealism of a given picture.

• Type: Conference
• Year: 2003
• Where: CVPR
• Authors: Florin Cutzu, Riad I. Hammoud, Alex Leykin
• Keywords: Automatic Classifier, Certain Gray-scale-texture Measurements, Computer Vision, CVPR 2003, Perceptual Photorealism, Promising Classification Performance, Website Filtering Applications