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2014 IEEE Winter Conference on Applications of Computer Vision (WACV) (2014)
Steamboat Springs, CO, USA
March 24, 2014 to March 26, 2014
ISBN: 978-1-4799-4985-4
pp: 347-354
Juha Ylioinas , Center for Machine Vision Research, University of Oulu, Finland
Juho Kannala , Center for Machine Vision Research, University of Oulu, Finland
Abdenour Hadid , Center for Machine Vision Research, University of Oulu, Finland
Matti Pietikainen , Center for Machine Vision Research, University of Oulu, Finland
ABSTRACT
In this paper we propose a unified framework for learning such local image descriptors that describe pixel neighborhoods using binary codes. The descriptors are constructed using binary decision trees which are learnt from a set of training image patches. Our framework generalizes several previously proposed binary descriptors, such as BRIEF, LBP and their variants, and provides a principled way to learn new constructions which have not been previously studied. Further, the proposed framework can utilize both labeled or unlabeled training data, and hence fits to both supervised and unsupervised learning scenarios. We evaluate our framework using varying levels of supervision in the learning phase. The experiments show that our descriptor constructions perform comparably to benchmark descriptors in two different applications, namely texture categorization and age group classification from facial images.
INDEX TERMS
Decision trees, Training, Geometry, Robustness, Accuracy, Materials, Entropy
CITATION

J. Ylioinas, J. Kannala, A. Hadid and M. Pietikainen, "Learning local image descriptors using binary decision trees," 2014 IEEE Winter Conference on Applications of Computer Vision (WACV), Steamboat Springs, CO, USA, 2014, pp. 347-354.
doi:10.1109/WACV.2014.6836079
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