Painting Analysis Using Wavelets and Probabilistic Topic Models
| Autor: | William Brown, David Steel, Gungor Polatkan, Robert Calderbank, Tong Wu, Ingrid Daubechies |
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| Jazyk: | angličtina |
| Rok vydání: | 2014 |
| Předmět: |
Topic model
FOS: Computer and information sciences business.industry Computer science Computer Vision and Pattern Recognition (cs.CV) Feature extraction Probabilistic logic ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Computer Science - Computer Vision and Pattern Recognition Wavelet transform Pattern recognition Machine Learning (stat.ML) Hierarchical clustering Machine Learning (cs.LG) Computer Science - Learning Wavelet Statistics - Machine Learning Unsupervised learning Artificial intelligence Complex wavelet transform business Hidden Markov model ComputingMethodologies_COMPUTERGRAPHICS |
| Zdroj: | ICIP |
| Popis: | 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. 5 pages, 4 figures, ICIP 2013 |
| Databáze: | OpenAIRE |
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