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
A perceptual grouping hierarchy for appearance-based 3D object recognition
Computer Vision and Image Understanding - Special issue on perceptual organization in computer vision
Finding Line Segments by Stick Growing
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
A Cubist Approach to Object Recognition
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Selection of Scale-Invariant Parts for Object Class Recognition
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 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
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Local image descriptors have been widely researched and used, due to their resistance to clutter and partial occlusion, as well as their partial insensitivity to object pose. Recently Mikolajczyk and Schmid [1] compared a number of such descriptors and concluded that the SIFT-based ones perform best in image matching tasks. This paper compares the effect that three local descriptors have on object recognition: SIFT [2], PCA-SIFT [3] and keyed context patches [4]. We use a data set containing images of six objects on clean and cluttered backgrounds, taken around the whole viewing sphere. We conclude that keyed context patches perform best overall, but they are outperformed for some objects by the second best feature, PCA-SIFT.