Introduction to Algorithms
On the Resemblance and Containment of Documents
SEQUENCES '97 Proceedings of the Compression and Complexity of Sequences 1997
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Photo tourism: exploring photo collections in 3D
ACM SIGGRAPH 2006 Papers
Scalable Recognition with a Vocabulary Tree
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Keypoint Recognition Using Randomized Trees
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Approximate Reasoning
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
Weakly supervised learning of part-based spatial models for visual object recognition
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
ShadowDraw: real-time user guidance for freehand drawing
ACM SIGGRAPH 2011 papers
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Patch descriptors are used for a variety of tasks ranging from finding corresponding points across images, to describing object category parts. In this paper, we propose an image patch descriptor based on edge position, orientation and local linear length. Unlike previous works using histograms of gradients, our descriptor does not encode relative gradient magnitudes. Our approach locally normalizes the patch gradients to remove relative gradient information, followed by orientation dependent binning. Finally, the edge histogram is binarized to encode edge locations, orientations and lengths. Two additional extensions are proposed for fast PCA dimensionality reduction, and a min-hash approach for fast patch retrieval. Our algorithm produces state-of-the-art results on previously published object instance patch data sets, as well as a new patch data set modeling intra-category appearance variations.