Ten lectures on wavelets
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Machine Vision and Applications
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
CAIP '09 Proceedings of the 13th International Conference on Computer Analysis of Images and Patterns
FINE: Fisher Information Nonparametric Embedding
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detection of forgery in paintings using supervised learning
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Wavelet-based statistical signal processing using hidden Markovmodels
IEEE Transactions on Signal Processing
Bayesian tree-structured image modeling using wavelet-domain hidden Markov models
IEEE Transactions on Image Processing
Studying digital imagery of ancient paintings by mixtures of stochastic models
IEEE Transactions on Image Processing
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The new field of visual stylometry proposes to apply mathematical and statistical tools to high-resolution images of artworks to produce a quantitative description of each work's style, or of stylistic differences between works. Such quantitative evidence regarding artistic style can then assist in addressing open art historical questions, including those of a particular work's authorship or date of creation. In this paper, we develop a new technique for visual stylometry on impressionist and/or post-impressionist paintings. We focus on the background of each painting for our analysis, hypothesizing that only this bears the signature of the artist's hand. We then introduce a new wavelet-Hidden-Markov-Tree-based Fisher information distance as a metric of stylistic similarity between brushwork samples. Tests on two datasets consisting of over 100 impressionist and post-impressionist paintings by Van Gogh and contemporaries show that an unsupervised representation of the paintings according to this new metric tends to cluster the paintings by author and, within an author, by time period. Classifying paintings under leave-one-out cross-validation using coordinates in this unsupervised representation gave accuracies for artist classification of 87.7% and 85.0% for the two datasets and for time period classification of 81.5% and 75.4%, a substantial improvement over previously developed stylistic distance metrics for paintings.