TY - JOUR

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

UR - http://www.scopus.com/inward/record.url?scp=85054189287&partnerID=8YFLogxK

U2 - 10.1016/j.jvcir.2018.09.015

DO - 10.1016/j.jvcir.2018.09.015

M3 - Journal article

AN - SCOPUS:85054189287

VL - 56

SP - 220

EP - 233

JO - Journal of Visual Communication and Image Representation

JF - Journal of Visual Communication and Image Representation

SN - 1047-3203

ER -