An Efficient k-Means Clustering Algorithm: Analysis and Implementation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Clustering with Qualitative Information
FOCS '03 Proceedings of the 44th Annual IEEE Symposium on Foundations of Computer Science
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
Machine Learning
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 12 - Volume 12
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
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
Local Features for Object Class Recognition
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Object Categorization by Learned Universal Visual Dictionary
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Correlation clustering in general weighted graphs
Theoretical Computer Science - Approximation and online algorithms
ACM Transactions on Knowledge Discovery from Data (TKDD)
International Journal of Computer Vision
Learning semantic object parts for object categorization
Image and Vision Computing
Robust Object Detection with Interleaved Categorization and Segmentation
International Journal of Computer Vision
Local invariant feature detectors: a survey
Foundations and Trends® in Computer Graphics and Vision
The Pascal Visual Object Classes (VOC) Challenge
International Journal of Computer Vision
Adapted vocabularies for generic visual categorization
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
IEEE Transactions on Pattern Analysis and Machine Intelligence
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
Biologically inspired task oriented gist model for scene classification
Computer Vision and Image Understanding
| Hi-index | 0.00 |
We present a novel method for constructing a visual vocabulary that takes into account the class labels of images, thus resulting in better recognition performance and more efficient learning. Our method consists of two stages: Cluster Precision Maximisation (CPM) and Adaptive Refinement. In the first stage, a Reciprocal Nearest Neighbours (RNN) clustering algorithm is guided towards class representative visual words by maximising a new cluster precision criterion. As we are able to optimise the vocabulary without the need for expensive cross-validation, the overall training time is significantly reduced without a negative impact on the results. Next, an adaptive threshold refinement scheme is proposed with the aim of increasing vocabulary compactness while at the same time improving the recognition rate and further increasing the representativeness of the visual words for category-level object recognition. This is a correlation clustering based approach, which works as a meta-clustering and optimises the cut-off threshold for each cluster separately. In the experiments we analyse the recognition rate of different vocabularies for a subset of the Caltech 101 dataset, showing how RNN in combination with CPM selects the optimal codebooks, and how the clustering refinement step succeeds in further increasing the recognition rate.