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Painting Analysis Using Wavelets and Probabilistic Topic Models

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Paper breakdownThe authors' claims, conditions and measurements, as JSON Signed receiptProof this review has not been altered
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Tong Wu, Gungor Polatkan, David Steel, William Brown, Ingrid Daubechies, Robert Calderbank

Computer Vision and Pattern Recognition · Submitted 26 Jan 2014

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim Results suggest that such unsupervised probabilistic topic models can be useful to distill characteristic elements of style.

how it is stated · hedged possibility 1.00 read the record →

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arXiv ID1401.6638 (version 1)
Pith ID76VDMOCQ, Pith's permanent record of this paper and its review
CitationDavid Steel, Gungor Polatkan, Ingrid Daubechies, Robert Calderbank, Tong Wu, William Brown. "Painting Analysis Using Wavelets and Probabilistic Topic Models." arXiv:1401.6638 (2014). https://pith.science/paper/76VDMOCQ
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The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13-20260917 via OpenRouter, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,271 input tokens, $0.0002 reported.

What the abstract itself states, as probabilities: claims first 0.03 · states limitation 0.12 · human participants 0.02 · controlled comparison 0.04 · randomized assignment 0.01 · states quantitative result 0.06 · compares with prior results 0.06 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

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.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 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. contribution 0.94 0.85 0.03 asserted 0.96
s1 Features are extracted by combining a dual-tree complex wavelet transform with a hidden Markov tree (HMT) model. method 1.00 0.86 0.02 asserted 1.00
s2 Hierarchical clustering is used to identify stylistic keywords in image patches, and keyword frequencies are calculated for sub-images that each contains many patches. method 1.00 0.89 0.03 asserted 1.00
s3 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. method 0.96 0.89 0.24 asserted 0.93
s4 Results suggest that such unsupervised probabilistic topic models can be useful to distill characteristic elements of style. interpretation 0.95 0.82 0.84 hedged possibility 1.00

What kind of paper the classifier reports this is: method or tool 0.89 · evidence from analysis of existing data 0.47.

What was examined, and what was not

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  • figures and tables
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  • P abstract
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  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original 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.

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