NDLI: Painting analysis using wavelets and probabilistic topic models

Content Provider	IEEE Xplore Digital Library
Author	Tong Wu Polatkan, G. Steel, D. Brown, W. Daubechies, I. Calderbank, R.
Copyright Year	2013
Description	Author affiliation: Dept. of Comput. Sci., Duke Univ., Durham, NH, USA (Calderbank, R.) \|\| North Carolina Museum of Art, NC, USA (Polatkan, G.; Steel, D.; Brown, W.) \|\| Dept. of Electr. & Comput. Eng., Rutgers Univ., Piscataway, NJ, USA (Tong Wu) \|\| Dept. of Math., Duke Univ., Durham, NH, USA (Daubechies, I.)
Abstract	In this paper, computer-based techniques for stylistic analysis of paintings are applied to the five panels of the 14th century Peruzzi Altarpiece by Giotto di Bondone. Features are extracted by combining a dual-tree complex wavelet transform with a hidden Markov tree (HMT) model. Hierarchical clustering is used to identify stylistic keywords in image patches, and keyword frequencies are calculated for sub-images that each contains many patches. A generative hierarchical Bayesian model learns stylistic patterns of keywords; these patterns are then used to characterize the styles of the sub-images; this in turn, permits to discriminate between paintings. Results suggest that such unsupervised probabilistic topic models can be useful to distill characteristic elements of style.
Starting Page	3264
Ending Page	3268
File Size	737842
Page Count	5
File Format	PDF
ISBN	9781479923410
DOI	10.1109/ICIP.2013.6738672
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-09-15
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Machine Learning Painting Analysis Wavelet Transforms Hidden Markov Trees Topic Models
Content Type	Text
Resource Type	Article

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