CSDL Home IEEE Transactions on Pattern Analysis & Machine Intelligence 2012 vol.34 Issue No.11 - Nov.
Issue No.11 - Nov. (2012 vol.34)
J. M. Hughes , Dept. of Comput. Sci., Dartmouth Coll., Hanover, NH, USA
Dong Mao , Dept. of Math., Michigan State Univ., East Lansing, MI, USA
D. N. Rockmore , Dept. of Math., Dartmouth Coll., Hanover, NH, USA
Yang Wang , Dept. of Math., Michigan State Univ., East Lansing, MI, USA
Qiang Wu , Dept. of Math. Sci., Middle Tennessee Univ., Murfreesboro, TN, USA
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TPAMI.2012.16
In this paper, we show how the tools of empirical mode decomposition (EMD) analysis can be applied to the problem of “visual stylometry,” generally defined as the development of quantitative tools for the measurement and comparisons of individual style in the visual arts. In particular, we introduce a new form of EMD analysis for images and show that it is possible to use its output as the basis for the construction of effective support vector machine (SVM)-based stylometric classifiers. We present the methodology and then test it on collections of two sets of digital captures of drawings: a set of authentic and well-known imitations of works attributed to the great Flemish artist Pieter Bruegel the Elder (1525-1569) and a set of works attributed to Dutch master Rembrandt van Rijn (1606-1669) and his pupils. Our positive results indicate that EMD-based methods may hold promise generally as a technique for visual stylometry.
support vector machines, art, image classification, visual arts, empirical mode decomposition analysis, visual stylometry, EMD analysis, support vector machine-based stylometric classifiers, SVM, drawing digital captures, Visualization, Art, Shape, Kernel, Vectors, Wavelet analysis, Electronic mail, image processing., Empirical mode decomposition, stylometry, classifier
J. M. Hughes, Dong Mao, D. N. Rockmore, Yang Wang, Qiang Wu, "Empirical Mode Decomposition Analysis for Visual Stylometry", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.34, no. 11, pp. 2147-2157, Nov. 2012, doi:10.1109/TPAMI.2012.16