Junctions: Detection, Classification, and Reconstruction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Detection with Automatic Scale Selection
International Journal of Computer Vision
Saliency, Scale and Image Description
International Journal of Computer Vision
Scale-Space Theory in Computer Vision
Scale-Space Theory in Computer Vision
A Framework for Low Level Feature Extraction
ECCV '94 Proceedings of the Third European Conference-Volume II on Computer Vision - Volume II
An Affine Invariant Interest Point Detector
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
IEEE Transactions on Pattern Analysis and Machine Intelligence
Matching Widely Separated Views Based on Affine Invariant Regions
International Journal of Computer Vision
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
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
A Comparison of Affine Region Detectors
International Journal of Computer Vision
Multiple Object Class Detection with a Generative Model
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Efficient Maximally Stable Extremal Region (MSER) Tracking
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
3D Segmentation by Maximally Stable Volumes (MSVs)
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Local invariant feature detectors: a survey
Foundations and Trends® in Computer Graphics and Vision
Linear Time Maximally Stable Extremal Regions
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Objects, Abstraction, Data Structures and Design: Using C++
Objects, Abstraction, Data Structures and Design: Using C++
IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 2
Coding Images with Local Features
International Journal of Computer Vision
Context-Aware Saliency Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
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Local image features are often used to efficiently represent image content. The limited number of types of features that a local feature extractor responds to might be insufficient to provide a robust image representation. To overcome this limitation, we propose a context-aware feature extraction formulated under an information theoretic framework. The algorithm does not respond to a specific type of features; the idea is to retrieve complementary features which are relevant within the image context. We empirically validate the method by investigating the repeatability, the completeness, and the complementarity of context-aware features on standard benchmarks. In a comparison with strictly local features, we show that our context-aware features produce more robust image representations. Furthermore, we study the complementarity between strictly local features and context-aware ones to produce an even more robust representation.