Normalized Cuts and Image Segmentation
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Context-based vision system for place and object recognition
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Robust Real-Time Face Detection
International Journal of Computer Vision
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Computer analysis of Van Gogh's complementary colours
Pattern Recognition Letters
Feature selection for paintings classification by optimal tree pruning
MRCS'06 Proceedings of the 2006 international conference on Multimedia Content Representation, Classification and Security
Informational dialogue with van Gogh's paintings
Computational Aesthetics'08 Proceedings of the Fourth Eurographics conference on Computational Aesthetics in Graphics, Visualization and Imaging
Perceptual and computational categories in art
Computational Aesthetics'08 Proceedings of the Fourth Eurographics conference on Computational Aesthetics in Graphics, Visualization and Imaging
Application of bilateral filtering and Gaussian mixture modeling for the retrieval of paintings
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Sum-of-superellipses: a low parameter model for amplitude spectra of natural images
ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing: Part I
Geographical analysis of the vernacular
Journal of Information Science
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Untrained observers readily cluster paintings from different art periods into distinct groups according to their overall visual appearance or 'look' [WCF08]. These clusters are typically influenced by both the content of the paintings (e.g. portrait, landscape, still-life, etc.), and stylistic considerations (e.g. the 'flat' appearance of Gothic paintings, or the distinctive use of colour in Fauve works). Here we aim to identify a set of image measurements that can capture this 'naïve visual impression of art', and use these features to automatically cluster a database of images of paintings into appearance-based groups, much like an untrained observer. We combine a wide range of features from simple colour statistics, through mid-level spatial features to high-level properties, such as the output of face-detection algorithms, which are intended to correlate with semantic content. Together these features yield clusters of images that look similar to one another despite differences in historical period and content. In addition, we tested the performance of the feature library in several classification tasks yielding good results. Our work could be applied as a curatorial or research aid, and also provides insight into the image attributes that untrained subjects may attend to when judging works of art.