Artistic movement recognition by consensus of boosted SVM based experts

Corneliu Florea; Fabian Gieseke

doi:10.1016/j.jvcir.2018.09.015

Artistic movement recognition by consensus of boosted SVM based experts

Corneliu Florea^*, Fabian Gieseke

^*Corresponding author for this work

6 Citations (Scopus)

Abstract

In this work we aim to automatically recognize the artistic movement from a digitized image of a painting. Our approach uses a new system that resorts to descriptions induced by color structure histograms and by novel topographical features for texture assessment. The topographical descriptors accumulate information from the first and second local derivatives within four layers of finer representations. The classification is performed by two layers of ensembles. The first is an adapted boosted ensemble of support vector machines, which introduces further randomization over feature categories as a regularization. The training of the ensemble yields individual experts by isolating initially misclassified images and by correcting them in further stages of the process. The solution improves the performance by a second layer build upon the consensus of multiple local experts that analyze different parts of the images. The resulting performance compares favorably with classical solutions and manages to match the ones of modern deep learning frameworks.

Original language	English
Journal	Journal of Visual Communication and Image Representation
Volume	56
Pages (from-to)	220-233
ISSN	1047-3203
DOIs	https://doi.org/10.1016/j.jvcir.2018.09.015
Publication status	Published - 2018

Keywords

Consensus of experts
Ensembles
Multi-scale topography
Painting style recognition
Randomized boosted SVMs

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10.1016/j.jvcir.2018.09.015

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T1 - Artistic movement recognition by consensus of boosted SVM based experts

AU - Florea, Corneliu

AU - Gieseke, Fabian

PY - 2018

Y1 - 2018

N2 - In this work we aim to automatically recognize the artistic movement from a digitized image of a painting. Our approach uses a new system that resorts to descriptions induced by color structure histograms and by novel topographical features for texture assessment. The topographical descriptors accumulate information from the first and second local derivatives within four layers of finer representations. The classification is performed by two layers of ensembles. The first is an adapted boosted ensemble of support vector machines, which introduces further randomization over feature categories as a regularization. The training of the ensemble yields individual experts by isolating initially misclassified images and by correcting them in further stages of the process. The solution improves the performance by a second layer build upon the consensus of multiple local experts that analyze different parts of the images. The resulting performance compares favorably with classical solutions and manages to match the ones of modern deep learning frameworks.

AB - In this work we aim to automatically recognize the artistic movement from a digitized image of a painting. Our approach uses a new system that resorts to descriptions induced by color structure histograms and by novel topographical features for texture assessment. The topographical descriptors accumulate information from the first and second local derivatives within four layers of finer representations. The classification is performed by two layers of ensembles. The first is an adapted boosted ensemble of support vector machines, which introduces further randomization over feature categories as a regularization. The training of the ensemble yields individual experts by isolating initially misclassified images and by correcting them in further stages of the process. The solution improves the performance by a second layer build upon the consensus of multiple local experts that analyze different parts of the images. The resulting performance compares favorably with classical solutions and manages to match the ones of modern deep learning frameworks.

KW - Consensus of experts

KW - Ensembles

KW - Multi-scale topography

KW - Painting style recognition

KW - Randomized boosted SVMs

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DO - 10.1016/j.jvcir.2018.09.015

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EP - 233

JO - Journal of Visual Communication and Image Representation

JF - Journal of Visual Communication and Image Representation

ER -

Artistic movement recognition by consensus of boosted SVM based experts

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Keywords

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