A study of smoothing methods for language models applied to Ad Hoc information retrieval
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Novelty and redundancy detection in adaptive filtering
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Bayes and Pseudo-Bayes Estimates of Conditional Probabilities and Their Reliability
ECML '93 Proceedings of the European Conference on Machine Learning
The Journal of Machine Learning Research
Video Google: A Text Retrieval Approach to Object Matching in Videos
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
Cluster-based retrieval using language models
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
A Principled Approach to Detecting Surprising Events in Video
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A Bayesian Hierarchical Model for Learning Natural Scene Categories
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Batch mode active learning and its application to medical image classification
ICML '06 Proceedings of the 23rd international conference on Machine learning
Topic modeling: beyond bag-of-words
ICML '06 Proceedings of the 23rd international conference on Machine learning
LDA-based document models for ad-hoc retrieval
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Detecting Irregularities in Images and in Video
International Journal of Computer Vision
Image retrieval on large-scale image databases
Proceedings of the 6th ACM international conference on Image and video retrieval
Detecting Loop Closure with Scene Sequences
International Journal of Computer Vision
FAB-MAP: Probabilistic Localization and Mapping in the Space of Appearance
International Journal of Robotics Research
Introduction to Information Retrieval
Introduction to Information Retrieval
A Generative Theory of Relevance
A Generative Theory of Relevance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian surprise and landmark detection
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Gaussian Processes for Object Categorization
International Journal of Computer Vision
International Journal of Robotics Research
Persistent Navigation and Mapping using a Biologically Inspired SLAM System
International Journal of Robotics Research
Identifying surprising events in videos using bayesian topic models
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part III
Appearance-only SLAM at large scale with FAB-MAP 2.0
International Journal of Robotics Research
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
CAT-SLAM: probabilistic localisation and mapping using a continuous appearance-based trajectory
International Journal of Robotics Research
Fast and Incremental Method for Loop-Closure Detection Using Bags of Visual Words
IEEE Transactions on Robotics
Finding Rare Classes: Active Learning with Generative and Discriminative Models
IEEE Transactions on Knowledge and Data Engineering
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In this work, we present a novel approach that allows a robot to improve its own navigation performance through introspection and then targeted data retrieval. It is a step in the direction of life-long learning and adaptation and is motivated by the desire to build robots that have plastic competencies which are not baked in. They should react to and benefit from use. We consider a particular instantiation of this problem in the context of place recognition. Based on a topic-based probabilistic representation for images, we use a measure of perplexity to evaluate how well a working set of background images explain the robot's online view of the world. Offline, the robot then searches an external resource to seek out additional background images that bolster its ability to localize in its environment when used next. In this way the robot adapts and improves performance through use. We demonstrate this approach using data collected from a mobile robot operating in outdoor workspaces.