A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Representation of local geometry in the visual system
Biological Cybernetics
The Design and Use of Steerable Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Affine/ Photometric Invariants for Planar Intensity Patterns
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Multi-view Matching for Unordered Image Sets, or "How Do I Organize My Holiday Snaps?"
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Pruning Local Feature Correspondences Using Shape Context
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
A SIFT Descriptor with Global Context
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A Sparse Texture Representation Using Local Affine Regions
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
PCA-SIFT: a more distinctive representation for local image descriptors
CVPR'04 Proceedings of the 2004 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
Visual word disambiguation by semantic contexts
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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This paper proposes an augmented version of local feature that enhances the discriminative power of the feature without affecting its invariance to image deformations. The idea is about learning local features, aiming to estimate its semantic, which is then exploited in conjunction with the bag of words paradigm to build an augmented feature descriptor. Basically, any local descriptor can be casted in the proposed context, and thus the approach can be easy generalized to fit in with any local approach. The semantic-context signature is a 2D histogram which accumulates the spatial distribution of the visual words around each local feature. The obtained semantic-context component is concatenated with the local feature to generate our proposed feature descriptor. This is expected to handle ambiguities occurring in images with multiple similar motifs and depicting slight complicated non-affine distortions, outliers, and detector errors. The approach is evaluated for two data sets. The first one is intentionally selected with images containing multiple similar regions and depicting slight non-affine distortions. The second is the standard data set of Mikolajczyk. The evaluation results showed our approach performs significantly better than expected results as well as in comparison with other methods.