Learning a Sparse Representation for Object Detection
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Object Recognition with Informative Features and Linear Classification
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
Feature selection using linear classifier weights: interaction with classification models
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Feature Hierarchies for Object Classification
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Creating Efficient Codebooks for Visual Recognition
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Scalable Recognition with a Vocabulary Tree
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Rapid Biologically-Inspired Scene Classification Using Features Shared with Visual Attention
IEEE Transactions on Pattern Analysis and Machine Intelligence
FAB-MAP: Probabilistic Localization and Mapping in the Space of Appearance
International Journal of Robotics Research
Supervised Learning of Quantizer Codebooks by Information Loss Minimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Vision-based global localization and mapping for mobile robots
IEEE Transactions on Robotics
Histogram of Oriented Uniform Patterns for robust place recognition and categorization
International Journal of Robotics Research
Global localization with non-quantized local image features
Robotics and Autonomous Systems
Hierarchical Classifiers for Robust Topological Robot Localization
Journal of Intelligent and Robotic Systems
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This paper presents a novel appearance-based technique for qualitative spatial localization. A vocabulary of visual words is built automatically, representing local features that repeatedly occur in the set of training images. An information maximization technique is then applied to build a hierarchical classifier for each environment by learning informative visual words. Child nodes in this hierarchy encode information redundant with information coded by their parents. In localization, hierarchical classifiers are used in a top-down manner, where top-level visual words are examined first, and for each top-level visual word which does not respond as expected, its lower-level visual words are examined. This allows inference to recover from missing features encoded by higher-level visual words. Several experiments on a challenging localization database demonstrate the advantages of our hierarchical framework and show a significant improvement over the traditional bag-of-features approaches.