Artistic movement recognition by boosted fusion of color structure and topographic description

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Standard

Artistic movement recognition by boosted fusion of color structure and topographic description. / Florea, Corneliu; Toca, Cosmin; Gieseke, Fabian Cristian.

Proceedings - 2017 IEEE Winter Conference on Applications of Computer Vision. IEEE, 2017. p. 569-577.

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Harvard

Florea, C, Toca, C & Gieseke, FC 2017, Artistic movement recognition by boosted fusion of color structure and topographic description. in Proceedings - 2017 IEEE Winter Conference on Applications of Computer Vision. IEEE, pp. 569-577, 17th IEEE Winter Conference on Applications of Computer Vision, Santa Rosa, California, United States, 24/03/2017. https://doi.org/10.1109/WACV.2017.69

APA

Florea, C., Toca, C., & Gieseke, F. C. (2017). Artistic movement recognition by boosted fusion of color structure and topographic description. In Proceedings - 2017 IEEE Winter Conference on Applications of Computer Vision (pp. 569-577). IEEE. https://doi.org/10.1109/WACV.2017.69

Vancouver

Florea C, Toca C, Gieseke FC. Artistic movement recognition by boosted fusion of color structure and topographic description. In Proceedings - 2017 IEEE Winter Conference on Applications of Computer Vision. IEEE. 2017. p. 569-577 https://doi.org/10.1109/WACV.2017.69

Author

Florea, Corneliu ; Toca, Cosmin ; Gieseke, Fabian Cristian. / Artistic movement recognition by boosted fusion of color structure and topographic description. Proceedings - 2017 IEEE Winter Conference on Applications of Computer Vision. IEEE, 2017. pp. 569-577

Bibtex

@inproceedings{e9da9b0a264444bca6c074743e051321,
title = "Artistic movement recognition by boosted fusion of color structure and topographic description",
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.",
author = "Corneliu Florea and Cosmin Toca and Gieseke, {Fabian Cristian}",
year = "2017",
month = may,
day = "11",
doi = "10.1109/WACV.2017.69",
language = "English",
pages = "569--577",
booktitle = "Proceedings - 2017 IEEE Winter Conference on Applications of Computer Vision",
publisher = "IEEE",
note = "null ; Conference date: 24-03-2017 Through 31-03-2017",

}

RIS

TY - GEN

T1 - Artistic movement recognition by boosted fusion of color structure and topographic description

AU - Florea, Corneliu

AU - Toca, Cosmin

AU - Gieseke, Fabian Cristian

N1 - Conference code: 17

PY - 2017/5/11

Y1 - 2017/5/11

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85020228047&partnerID=8YFLogxK

U2 - 10.1109/WACV.2017.69

DO - 10.1109/WACV.2017.69

M3 - Article in proceedings

AN - SCOPUS:85020228047

SP - 569

EP - 577

BT - Proceedings - 2017 IEEE Winter Conference on Applications of Computer Vision

PB - IEEE

Y2 - 24 March 2017 through 31 March 2017

ER -

ID: 179556482