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Multi-task learning via non-sparse multiple kernel learning

: Samek, W.; Binder, A.; Kawanabe, M.


Real, P.:
Computer analysis of images and patterns. Proceedings Pt.1 : 14th international conference, CAIP 2011, Seville, Spain, August 29-31, 2011
Heidelberg: Springer, 2011 (Lecture Notes in Computer Science 6854)
ISBN: 3-642-23671-5
ISBN: 978-3-642-23671-6
ISBN: 978-3-642-23672-3
ISSN: 0302-9743
International Conference on Computer Analysis of Images and Patterns (CAIP) <14, 2011, Sevilla>
Conference Paper
Fraunhofer FIRST ()

In object classification tasks from digital photographs, multiple categories are considered for annotation. Some of these visual concepts may have semantic relations and can appear simultaneously in images. Although taxonomical relations and co-occurrence structures between object categories have been studied, it is not easy to use such information to enhance performance of object classification. In this paper, we propose a novel multi-task learning procedure which extracts useful information from the classifiers for the other categories. Our approach is based on non-sparse multiple kernel learning (MKL) which has been successfully applied to adaptive feature selection for image classification. Experimental results on PASCAL VOC 2009 data show the potential of our method.