Genetic operators for hierarchical graph clustering
Pattern Recognition Letters
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Pairwise Data Clustering by Deterministic Annealing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Self-organizing map for clustering in the graph domain
Pattern Recognition Letters - In memory of Professor E.S. Gelsema
Video Google: A Text Retrieval Approach to Object Matching in Videos
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
Central Clustering of Attributed Graphs
Machine Learning
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
Learning Shape-Classes Using a Mixture of Tree-Unions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discovering Shape Classes using Tree Edit-Distance and Pairwise Clustering
International Journal of Computer Vision
Graph embedding using tree edit-union
Pattern Recognition
International Journal of Computer Vision
Scene Classification Using a Hybrid Generative/Discriminative Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Describing Visual Scenes Using Transformed Objects and Parts
International Journal of Computer Vision
3D Object Recognition Using Hyper-Graphs and Ranked Local Invariant Features
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Constellations and the unsupervised learning of graphs
GbRPR'07 Proceedings of the 6th IAPR-TC-15 international conference on Graph-based representations in pattern recognition
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
Learning Class Specific Graph Prototypes
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
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The aim in this paper is to develop a method for clustering together image views of the same object class. Local invariant feature methods, such as SIFT, have been proven effective for image clustering. However, they have made either relatively little use or too complex use of geometric constraints and are confounded when the detected features are superabundant. Here we make two contributions aimed at overcoming these problems. First, we rank the SIFT points (R-SIFT) using visual saliency. Second, we use the reduced set of R-SIFT features to construct a specific hyper graph (CSHG) model of holistic-structure. Based on the CSHG model, a two stage clustering method is proposed. In which, images are clustered according to the pairwise similarity of the graphs, which is a combination of the traditional similarity of local invariant feature vectors and the geometric similarity between two graphs. This method comprehensively utilizes both SIFT and geometric constraints, and hence combines both global and local information. Experiments reveal that the method gives excellent clustering performance.