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
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
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
A Spectral Technique for Correspondence Problems Using Pairwise Constraints
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Object Categorization by Learned Universal Visual Dictionary
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Scalable Recognition with a Vocabulary Tree
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Spatial Weighting for Bag-of-Features
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Trains of keypoints for 3D object recognition
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
FAB-MAP: Probabilistic Localization and Mapping in the Space of Appearance
International Journal of Robotics Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improving Bag-of-Features for Large Scale Image Search
International Journal of Computer Vision
Location recognition using prioritized feature matching
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Modeling and Recognition of Landmark Image Collections Using Iconic Scene Graphs
International Journal of Computer Vision
Image retrieval with geometry-preserving visual phrases
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Total recall II: Query expansion revisited
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Three things everyone should know to improve object retrieval
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
From images to scenes: Compressing an image cluster into a single scene model for place recognition
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Speeded-up, relaxed spatial matching
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Negative evidences and co-occurences in image retrieval: the benefit of PCA and whitening
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
International Journal of Computer Vision
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This paper proposes a new framework for visual place recognition that incrementally learns models of each place and offers adaptability to dynamic elements in the scene. Traditional Bag-Of-Words (BOW) image-retrieval approaches to place recognition typically treat images in a holistic manner and are not capable of dealing with sub-scene dynamics, such as structural changes to a building façade or seasonal effects on foliage. However, by treating local features as observations of real-world landmarks in a scene that is observed repeatedly over a period of time, such dynamics can be modelled at a local level, and the spatio-temporal properties of each landmark can be independently updated incrementally. The method proposed models each place as a set of such landmarks and their geometric relationships. A new BOW filtering stage and geometric verification scheme are introduced to compute a similarity score between a query image and each scene model. As further training images are acquired for each place, the landmark properties are updated over time and in the long term, the model can adapt to dynamic behaviour in the scene. Results on an outdoor dataset of images captured along a 7 km path, over a period of 5 months, show an improvement in recognition performance when compared to state-of-the-art image retrieval approaches to place recognition.