Learning a Sparse Representation for Object Detection
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Learning to Detect Objects in Images via a Sparse, Part-Based Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Open source clustering software
Bioinformatics
Local Features for Object Class Recognition
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
One-Shot Learning of Object Categories
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
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
Pattern Recognition, Third Edition
Pattern Recognition, Third Edition
A boundary-fragment-model for object detection
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
CAT-SLAM: probabilistic localisation and mapping using a continuous appearance-based trajectory
International Journal of Robotics Research
| Hi-index | 0.00 |
In this work, we present a novel, fast clustering scheme for codebook generation from local features for object class recognition. It relies on a sequential data analysis and creates compact clusters with low variance. We compare our algorithm to other commonly used algorithms with respect to cluster statistics and classification performance. It turns out that our algorithm is the fastest for codebook generation, without loss in classification performance, when using the right matching scheme. In this context, we propose a well suited matching scheme for assigning data entries to cluster centers based on the sigmoid function.