The Design and Use of Steerable Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
The nature of statistical learning theory
The nature of statistical learning theory
Exact and approximation algorithms for clustering
Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Mean Shift, Mode Seeking, and Clustering
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
Support Vector Data Description
Machine Learning
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Convex Optimization
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
Hierarchical building recognition
Image and Vision Computing
Automatic Panoramic Image Stitching using Invariant Features
International Journal of Computer Vision
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
Local invariant feature detectors: a survey
Foundations and Trends® in Computer Graphics and Vision
Deformed Lattice Detection in Real-World Images Using Mean-Shift Belief Propagation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Matching and Retrieval by Repetitive Patterns
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
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Conventional object retrieval or recognition methods based on feature matching sometimes fail when an object contains repetitive patterns, because features from repetitive patterns are too similar to each other. Specifically, when there arise many similar features in a query object due to repetitive patterns, they are usually not matched to the ones at the same positions of the reference object. Hence, the matching pairs between the query and reference image do not appear regular and thus homography estimation fails. In case when we use ''nearest neighbor distance ratio'' as a matching criterion, where enough distinction between the matched pairs should be secured, matching also fails due to similarity of features. In this paper, we propose a new feature matching strategy to alleviate this problem by discriminating repetitive patterns from the other salient ones and also by developing a way of utilizing the patterns for robust feature matching. Specifically, we first apply a conventional feature extraction method to a given image. Then we cluster features based on their similarity, i.e., we design a classifier that tells whether a feature is from a repetitive pattern or from a salient structure. For the effective use of repetitive patterns, we define a new descriptor based on support vector data description (SVDD) for describing clusters of similar features. In other words, a set of features from a pattern is defined to be a new feature in terms of its center and radius. For object recognition, the homography is found over the salient features by excluding repetitive features at first, which is then validated and refined by the repetitive patterns. The proposed method is tested with examples of matching buildings with repetitive patterns, and it is shown to be robuster and more reliable than the conventional methods.