International Journal of Computer Vision
Feature Detection with Automatic Scale Selection
International Journal of Computer Vision
The Topological Structure of Scale-Space Images
Journal of Mathematical Imaging and Vision
Hierarchical morse complexes for piecewise linear 2-manifolds
SCG '01 Proceedings of the seventeenth annual symposium on Computational geometry
A Probabilistic Approach to Object Recognition Using Local Photometry and Global Geometry
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
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
Pictorial Structures for Object Recognition
International Journal of Computer Vision
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
Deformation Invariant Image Matching
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
The Pyramid Match Kernel: Efficient Learning with Sets of Features
The Journal of Machine Learning Research
Multi-resolution Morse-Smale Complexes for Terrain Modeling
ICIAP '07 Proceedings of the 14th International Conference on Image Analysis and Processing
The Representation and Matching of Pictorial Structures
IEEE Transactions on Computers
Local invariant feature detectors: a survey
Foundations and Trends® in Computer Graphics and Vision
Efficient visual object tracking with online nearest neighbor classifier
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part I
Improving three-dimensional point reconstruction from image correspondences using surface curvatures
Machine Vision and Applications
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We propose new ideas and efficient algorithms towards bridging the gap between bag-of-features and constellation descriptors for image matching. Specifically, we show how to compute connections between local image features in the form of a critical net whose construction is repeatable across changes of viewing conditions or scene configuration. Arcs of the net provide a more reliable frame of reference than individual features do for the purpose of invariance. In addition, regions associated with either small stars or loops in the critical net can be used as parts for recognition or retrieval, and subgraphs of the critical net that are matched across images exhibit common structures shared by different images. We also introduce the notion of beta-stable features, a variation on the notion of feature lifetime from the literature of scale space. Our experiments show that arc-based SIFT-like descriptors of beta-stable features are more repeatable and more accurate than competing descriptors. We also provide anecdotal evidence of the usefulness of image parts and of the structures that are found to be common across images.