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
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
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Pairwise Similarity Propagation Based Graph Clustering for Scalable Object Indexing and Retrieval
GbRPR '09 Proceedings of the 7th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition
Graph-Based Object Class Discovery
CAIP '09 Proceedings of the 13th International Conference on Computer Analysis of Images and Patterns
Learning Class Specific Graph Prototypes
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
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 focusses on the problem of locating object class exemplars from a large corpus of images using affinity propagation. We use attributed relational graphs to represent groups of local invariant features together with their spatial arrangement. Rather than mining exemplars from the entire graph corpus, we prefer to cluster object specific exemplars. Firstly, we obtain an object specific cluster of graphs using a similarity propagation based graph clustering (SPGC) method. Here a SOM neural net based tree clustering method is used to incrementally cluster a large corpus of local invariant descriptors. The popular affinity propagation based clustering algorithm is then individually applied to each object specific cluster. Using this clustering method, we obtain object specific exemplars together with a high precision for the data associated with each exemplar. The strategy adopted is one of divide and conquer, and this greatly increases the efficiency of mining exemplars. Using the exemplars, we perform recognition using a majority voting strategy that is weighted by nearest neighbor similarity. Experiments are performed on over 80K images spanning ∼500 objects, and demonstrate the performance in terms of efficiency, scalability and recognition.