International Journal of Computer Vision
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Non-parametric Local Transforms for Computing Visual Correspondence
ECCV '94 Proceedings of the Third European Conference-Volume II on Computer Vision - Volume II
Catadioptric Omnidirectional Camera
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Context-based vision system for place and object recognition
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
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
Robust Foreground Detection In Video Using Pixel Layers
IEEE Transactions on Pattern Analysis and Machine Intelligence
FAB-MAP: Probabilistic Localization and Mapping in the Space of Appearance
International Journal of Robotics Research
A sequential dual method for large scale multi-class linear svms
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
COLD: The CoSy Localization Database
International Journal of Robotics Research
A realistic benchmark for visual indoor place recognition
Robotics and Autonomous Systems
Visual topological SLAM and global localization
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Object recognition from omnidirectional visual sensing for mobile robot applications
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Visual place categorization: problem, dataset, and algorithm
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Multi-modal Semantic Place Classification
International Journal of Robotics Research
A review of log-polar imaging for visual perception in robotics
Robotics and Autonomous Systems
CVPR'03 Proceedings of the 2003 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
Sampling strategies for bag-of-features image classification
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Fast scene change detection using direct feature extraction fromMPEG compressed videos
IEEE Transactions on Multimedia
Modified phase-correlation based robust hard-cut detection with application to archive film
IEEE Transactions on Circuits and Systems for Video Technology
Multi-cue based place learning for mobile robot navigation
AIS'12 Proceedings of the Third international conference on Autonomous and Intelligent Systems
Bubble space and place representation in topological maps
International Journal of Robotics Research
Integrating cue descriptors in bubble space for place recognition
ICVS'13 Proceedings of the 9th international conference on Computer Vision Systems
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We describe a new semantic descriptor for robots to recognize visual places. The descriptor integrates image features and color information via the hull census transform (HCT) and image histogram indexing. Our approach extracts the semantic description based on the convex hull points and statistical calculation. The color histograms are then formed by four indices and added to the descriptor. The semantic codebook consists of several places with many image descriptors. Finally, a one-versus-one (OVO) multi-class support vector machine (SVM) is used to model the places. The proposed technique is achieved by using a high-level cue integration scheme based on the learning information over the color and feature space to optimally combine the weighted cues. It is suitable for visual place recognition, particularly for the images captured by an omnidirectional camera. The experimental results show that the codebook with less vectors is as robust as most popular codebooks under varying environments. The performance is evaluated and compared with several state-of-the-art descriptors.