TY - JOUR

T1 - Insights from classifying visual concepts with multiple kernel learning

AU - Binder, Alexander

AU - Nakajima, Shinichi

AU - Kloft, Marius

AU - Müller, Christina

AU - Samek, Wojciech

AU - Brefeld, Ulf

AU - Müller, Klaus Robert

AU - Kawanabe, Motoaki

PY - 2012/8/24

Y1 - 2012/8/24

N2 - Combining information from various image features has become a standard technique in concept recognition tasks. However, the optimal way of fusing the resulting kernel functions is usually unknown in practical applications. Multiple kernel learning (MKL) techniques allow to determine an optimal linear combination of such similarity matrices. Classical approaches to MKL promote sparse mixtures. Unfortunately, 1-norm regularized MKL variants are often observed to be outperformed by an unweighted sum kernel. The main contributions of this paper are the following: we apply a recently developed non-sparse MKL variant to state-of-the-art concept recognition tasks from the application domain of computer vision. We provide insights on benefits and limits of non-sparse MKL and compare it against its direct competitors, the sum-kernel SVM and sparse MKL. We report empirical results for the PASCAL VOC 2009 Classification and ImageCLEF2010 Photo Annotation challenge data sets. Data sets (kernel matrices) as well as further information are available at http://doc.ml.tuberlin.de/image_mkl/(Accessed 2012 Jun 25).

AB - Combining information from various image features has become a standard technique in concept recognition tasks. However, the optimal way of fusing the resulting kernel functions is usually unknown in practical applications. Multiple kernel learning (MKL) techniques allow to determine an optimal linear combination of such similarity matrices. Classical approaches to MKL promote sparse mixtures. Unfortunately, 1-norm regularized MKL variants are often observed to be outperformed by an unweighted sum kernel. The main contributions of this paper are the following: we apply a recently developed non-sparse MKL variant to state-of-the-art concept recognition tasks from the application domain of computer vision. We provide insights on benefits and limits of non-sparse MKL and compare it against its direct competitors, the sum-kernel SVM and sparse MKL. We report empirical results for the PASCAL VOC 2009 Classification and ImageCLEF2010 Photo Annotation challenge data sets. Data sets (kernel matrices) as well as further information are available at http://doc.ml.tuberlin.de/image_mkl/(Accessed 2012 Jun 25).

KW - Informatics

KW - concept formation

KW - controlled study

KW - histogram

KW - image display

KW - intermethod comparison

KW - kernel method

KW - machine learning

KW - scoring system

KW - support vector machine

KW - task performance

KW - validity

KW - Business informatics

UR - http://www.scopus.com/inward/record.url?scp=84865281106&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/bcdfd7f6-4986-310b-b4f3-bbc1bfc4ce2e/

U2 - 10.1371/journal.pone.0038897

DO - 10.1371/journal.pone.0038897

M3 - Journal articles

C2 - 22936970

AN - SCOPUS:84865281106

VL - 7

JO - PLoS ONE

JF - PLoS ONE

SN - 1932-6203

IS - 8

M1 - e38897

ER -