The Design and Use of Steerable Filters
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
Selection of Scale-Invariant Parts for Object Class Recognition
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
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
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 12 - Volume 12
Multi-Image Matching Using Multi-Scale Oriented Patches
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Discriminative Training for Object Recognition Using Image Patches
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
The Pyramid Match Kernel: Discriminative Classification with Sets of Image Features
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
A Comparison of Affine Region Detectors
International Journal of Computer Vision
Multiple Object Class Detection with a Generative Model
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Scalable Recognition with a Vocabulary Tree
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Hierarchical building recognition
Image and Vision Computing
Evaluating bag-of-visual-words representations in scene classification
Proceedings of the international workshop on Workshop on multimedia information retrieval
Features for image retrieval: an experimental comparison
Information Retrieval
Learning Optimal Compact Codebook for Efficient Object Categorization
WACV '08 Proceedings of the 2008 IEEE Workshop on Applications of Computer Vision
NUS-WIDE: a real-world web image database from National University of Singapore
Proceedings of the ACM International Conference on Image and Video 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
Keyframe retrieval by keypoints: can point-to-point matching help?
CIVR'06 Proceedings of the 5th international conference on Image and Video Retrieval
Image matching based on representative local descriptors
MMM'10 Proceedings of the 16th international conference on Advances in Multimedia Modeling
Towards a universal and limited visual vocabulary
ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part II
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Bag-of-visual-words is a popular image representation that produces high matching accuracy and efficiency. While vocabulary size impacts on matching accuracy, existing research usually selects the vocabulary size empirically. Research on representative local descriptors shows that with similarity based clustering, the intra-cluster similarity extent of descriptors plays the same role in straightforward matching as vocabulary size in visual words matching. Based on this observation, we propose to use similarity based clustering to determine the optimal vocabulary size for a given dataset in visual words matching. Preliminary experiments with three datasets produce encouraging results and demonstrate the potential of the proposed approach.