Publication Type
Journal Article
Version
acceptedVersion
Publication Date
2-2009
Abstract
As one of the most important cultural heritages, classical western paintings have always played a special role in human live and been applied for many different purposes. While image classification is the subject of a plethora of related publications, relatively little attention has been paid to automatic categorization of western classical paintings which could be a key technique of modern digital library, museums and art galleries. This paper studies automatic classification on large western painting image collection. We propose a novel framework to support automatic classification on large western painting image collections. With this framework, multiple visual features can be integrated effectively to improve the accuracy of identification process significantly. We also evaluate our method and its competitors based on a large image collection. A careful study on the empirical results indicates the approach enjoys great superiority over the state-of-the-art approaches in different aspects.
Keywords
Classic western paintings, Identification, Image retrieval, Cultural heritage
Discipline
Databases and Information Systems | Numerical Analysis and Scientific Computing
Research Areas
Data Science and Engineering
Publication
Pattern Recognition
Volume
42
Issue
2
First Page
293
Last Page
301
ISSN
0031-3203
Identifier
10.1016/j.patcog.2008.04.016
Publisher
Elsevier
Citation
SHEN, Jialie.
Stochastic Modeling Western Paintings for Effective Classification. (2009). Pattern Recognition. 42, (2), 293-301.
Available at: https://ink.library.smu.edu.sg/sis_research/758
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1016/j.patcog.2008.04.016
Included in
Databases and Information Systems Commons, Numerical Analysis and Scientific Computing Commons