Machine Learning
Machine Learning
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
On Clustering Using Random Walks
FST TCS '01 Proceedings of the 21st Conference on Foundations of Software Technology and Theoretical Computer Science
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Discovering Objects and their Localization in Images
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Unsupervised Learning of Categories from Sets of Partially Matching Image Features
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
Computer Vision and Image Understanding
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Unsupervised Object Discovery: A Comparison
International Journal of Computer Vision
Localizing objects while learning their appearance
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Learning the easy things first: Self-paced visual category discovery
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Ensemble of exemplar-SVMs for object detection and beyond
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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While the quality of object recognition systems can strongly benefit from more data, human annotation and labeling can hardly keep pace. This motivates the usage of autonomous and unsupervised learning methods. In this paper, we present a simple, yet effective method for unsupervised image categorization, which relies on discriminative learners. Since automatically obtaining error-free labeled training data for the learners is infeasible, we propose the concept of weak training (WT) set. WT sets have various deficiencies, but still carry useful information. Training on a single WT set cannot result in good performance, thus we design a random walk sampling scheme to create a series of diverse WT sets. This naturally allows our categorization learning to leverage ensemble learning techniques. In particular, for each WT set, we train a max-margin classifier to further partition the whole dataset to be categorized. By doing so, each WT set leads to a base partitioning of the dataset and all the base partitionings are combined into an ensemble proximity matrix. The final categorization is completed by feeding this proximity matrix into a spectral clustering algorithm. Experiments on a variety of challenging datasets show that our method outperforms competing methods by a considerable margin.