Readings in computer vision: issues, problems, principles, and paradigms
Local Grayvalue Invariants for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Prototype selection for composite nearest neighbor classifiers
Prototype selection for composite nearest neighbor classifiers
Feature Detection with Automatic Scale Selection
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
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
Evaluation campaigns and TRECVid
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
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
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This paper describes a method of introducing spatial consistency constraints in the process of matching set-based descriptors extracted from digital images. The proposed matching technique is guided by a rule that can be summarized as follows: a descriptor is important for the match if it is similar to some descriptor from the other image and its spatial neighbors are important. The resulting match is partial in the sense that it deliberately avoids the complexity of searching for one-to-one correspondences among particular descriptors, but established affinity among groups of descriptors instead. Formally, the proposed method is expressed as an eigenvalue problem, where the principal eigenvector's components render the importance values of individual descriptors, while the corresponding eigenvalue represents an estimate of the overall strength of affinity between images being matched. These measures of descriptor importance and image affinity are shown to provide a natural basis for intra- and inter-image prototype selection. Several variations of the proposed technique are empirically evaluated on the task of content-based image retrieval, demonstrating encouraging results.