Painting Analysis Using Wavelets and Probabilistic Topic Models

by   Tong Wu, et al.
North Carolina Department of Natural and Cultural Resources
Duke University
Rutgers University

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.


Combining Thesaurus Knowledge and Probabilistic Topic Models

In this paper we present the approach of introducing thesaurus knowledge...

Keyword-based Topic Modeling and Keyword Selection

Certain type of documents such as tweets are collected by specifying a s...

Keyword Assisted Topic Models

For a long time, many social scientists have conducted content analysis ...

MPTopic: Improving topic modeling via Masked Permuted pre-training

Topic modeling is pivotal in discerning hidden semantic structures withi...

Query Generation for Patent Retrieval with Keyword Extraction based on Syntactic Features

This paper describes a new method to extract relevant keywords from pate...

4D Dual-Tree Complex Wavelets for Time-Dependent Data

The dual-tree complex wavelet transform (DT-ℂWT) is extended to the 4D s...

Probabilistic Formal Modelling to Uncover and Interpret Interaction Styles

We present a study using new computational methods, based on a novel com...

Please sign up or login with your details

Forgot password? Click here to reset