An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Recognition with Local Features: the Kernel Recipe
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Context-based vision system for place and object recognition
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Object Recognition Using Composed Receptive Field Histograms of Higher Dimensionality
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
FAB-MAP: Probabilistic Localization and Mapping in the Space of Appearance
International Journal of Robotics Research
Cue integration through discriminative accumulation
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Support vector machines for histogram-based image classification
IEEE Transactions on Neural Networks
Overview of the CLEF 2009 robot vision track
CLEF'09 Proceedings of the 10th international conference on Cross-language evaluation forum: multimedia experiments
Multi-cue based place learning for mobile robot navigation
AIS'12 Proceedings of the Third international conference on Autonomous and Intelligent Systems
Hi-index | 0.00 |
In this paper we report on our successful participation in the RobotVision challenge in the ImageCLEF 2009 campaign. We present a place recognition system that employs four different discriminative models trained on different global and local visual cues. In order to provide robust recognition, the outputs generated by the models are combined using a discriminative accumulation method. Moreover, the system is able to provide an indication of the confidence of its decision. We analyse the properties and performance of the system on the training and validation data and report the final score obtained on the test run which ranked first in the obligatory track of the RobotVision task.