ML Anthology

Artistic Movement Recognition by Boosted Fusion of Color Structure and Topographic Description

Corneliu Florea, Cosmin Toca, Fabian Gieseke
WACV 2017 pp. 569-577
doi:10.1109/WACV.2017.69 /wacv/2017/florea2017wacv-artistic/

Abstract

We address the problem of automatically recognizing artistic movement in digitized paintings. We make the following contributions: Firstly, we introduce a large digitized painting database that contains refined annotations of artistic movement. Secondly, we propose a new system for the automatic categorization that resorts to image descriptions by color structure and novel topographical features as well as to an adapted boosted ensemble of support vector machines. The system manages to isolate initially misclassified images and to correct such errors in further stages of the boosting process. The resulting performance of the system compares favorably with classical solutions in terms of accuracy and even manages to outperform modern deep learning frameworks.

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Cite

Text

Florea et al. "Artistic Movement Recognition by Boosted Fusion of Color Structure and Topographic Description." IEEE/CVF Winter Conference on Applications of Computer Vision, 2017. doi:10.1109/WACV.2017.69

Markdown

[Florea et al. "Artistic Movement Recognition by Boosted Fusion of Color Structure and Topographic Description." IEEE/CVF Winter Conference on Applications of Computer Vision, 2017.](https://mlanthology.org/wacv/2017/florea2017wacv-artistic/) doi:10.1109/WACV.2017.69

BibTeX

@inproceedings{florea2017wacv-artistic,
  title     = {{Artistic Movement Recognition by Boosted Fusion of Color Structure and Topographic Description}},
  author    = {Florea, Corneliu and Toca, Cosmin and Gieseke, Fabian},
  booktitle = {IEEE/CVF Winter Conference on Applications of Computer Vision},
  year      = {2017},
  pages     = {569-577},
  doi       = {10.1109/WACV.2017.69},
  url       = {https://mlanthology.org/wacv/2017/florea2017wacv-artistic/}
}