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

Corneliu Florea; Cosmin Toca; Fabian Cristian Gieseke

doi:10.1109/WACV.2017.69

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

Corneliu Florea, Cosmin Toca, Fabian Cristian Gieseke

Department of Computer Science

14 Citations (Scopus)

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.

Original language	English
Title of host publication	Proceedings - 2017 IEEE Winter Conference on Applications of Computer Vision
Number of pages	9
Publisher	IEEE
Publication date	11 May 2017
Pages	569-577
ISBN (Electronic)	978-1-5090-4822-9
DOIs	https://doi.org/10.1109/WACV.2017.69
Publication status	Published - 11 May 2017
Event	17th IEEE Winter Conference on Applications of Computer Vision - Santa Rosa, United States Duration: 24 Mar 2017 → 31 Mar 2017 Conference number: 17

Conference

Conference	17th IEEE Winter Conference on Applications of Computer Vision
Number	17
Country/Territory	United States
City	Santa Rosa
Period	24/03/2017 → 31/03/2017

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10.1109/WACV.2017.69

Cite this

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

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

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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}",

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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.

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

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