title: Insights from classifying visual concepts with multiple kernel learning.
abstract: 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.tu-berlin.de/image_mkl/(Accessed 2012 Jun 25).
authors.affiliation: Machine Learning Group, Berlin Institute of Technology, Berlin, Germany. alexander.binder@tu-berlin.de
authors.forename: Alexander
authors.initials: A
authors.lastname: Binder
authors.forename: Shinichi
authors.initials: S
authors.lastname: Nakajima
authors.forename: Marius
authors.initials: M
authors.lastname: Kloft
authors.forename: Christina
authors.initials: C
authors.lastname: Müller
authors.forename: Wojciech
authors.initials: W
authors.lastname: Samek
authors.forename: Ulf
authors.initials: U
authors.lastname: Brefeld
authors.forename: Klaus-Robert
authors.initials: KR
authors.lastname: Müller
authors.forename: Motoaki
authors.initials: M
authors.lastname: Kawanabe
mesh_terms: D000465:Algorithms; D008962:Models, Theoretical; D010363:Pattern Recognition, Automated; D012984:Software