The nature of statistical learning theory
The nature of statistical learning theory
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Probability Estimates for Multi-class Classification by Pairwise Coupling
The Journal of Machine Learning Research
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Overview of the CLEF 2009 robot vision track
CLEF'09 Proceedings of the 10th international conference on Cross-language evaluation forum: multimedia experiments
Support vector machines for histogram-based image classification
IEEE Transactions on Neural Networks
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Topologically localizing a mobile robot using visual information alone is a difficult problem. We propose a localization system that comprises Gaussian derivatives as raw local descriptors, a three-tier spatial pyramid of histograms as the image descriptor, and probabilistic multi-class support vector machines for classification. Based on the probability estimate, the proposed system is able to predict the unknown class which corresponds to locations that are not imaged in the training sequence. To exploit the continuity of the sequence, a smoothing procedure can be applied, which is shown to be simple yet effective.