Computer Science – Computer Vision and Pattern Recognition
Scientific paper
2011-12-16
Computer Science
Computer Vision and Pattern Recognition
18 pages, 8 tables, 4 figures, format deviating from plos one submission format requirements for aesthetic reasons
Scientific paper
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, so-called 1-norm MKL variants are often observed to be outperformed by an unweighted sum kernel. The contribution of this paper is twofold: We apply a recently developed non-sparse MKL variant to state-of-the-art concept recognition tasks within 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 the sparse MKL. We report empirical results for the PASCAL VOC 2009 Classification and ImageCLEF2010 Photo Annotation challenge data sets. About to be submitted to PLoS ONE.
Binder Alexander
Brefeld Ulf
Kawanabe Motoaki
Kloft Marius
Müller Christina
No associations
LandOfFree
Insights from Classifying Visual Concepts with Multiple Kernel Learning does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Insights from Classifying Visual Concepts with Multiple Kernel Learning, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Insights from Classifying Visual Concepts with Multiple Kernel Learning will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-136303