Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Machine Learning
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Generic Object Recognition with Boosting
IEEE Transactions on Pattern Analysis and Machine Intelligence
An empirical comparison of supervised learning algorithms
ICML '06 Proceedings of the 23rd international conference on Machine learning
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
Journal of Cognitive Neuroscience
Random projection trees and low dimensional manifolds
STOC '08 Proceedings of the fortieth annual ACM symposium on Theory of computing
An empirical evaluation of supervised learning in high dimensions
Proceedings of the 25th international conference on Machine learning
Randomized Clustering Forests for Image Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Vlfeat: an open and portable library of computer vision algorithms
Proceedings of the international conference on Multimedia
The 2005 PASCAL visual object classes challenge
MLCW'05 Proceedings of the First international conference on Machine Learning Challenges: evaluating Predictive Uncertainty Visual Object Classification, and Recognizing Textual Entailment
Task-Driven Dictionary Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
IEEE Transactions on Information Theory
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Codebook learning is one of the central research topics in computer vision and machine learning. In this paper, we propose a new codebook learning algorithm, Randomized Forest Sparse Coding (RFSC), by harvesting the following three concepts: (1) ensemble learning, (2) divide-and-conquer, and (3) sparse coding. Given a set of training data, a randomized tree can be used to perform data partition (divide-and-conquer); after a tree is built, a number of bases are learned from the data within each leaf node for a sparse representation (subspace learning via sparse coding); multiple trees with diversities are trained (ensemble), and the collection of bases of these trees constitute the codebook. These three concepts in our codebook learning algorithm have the same target but with different emphasis: subspace learning via sparse coding makes a compact representation, and reduces the information loss; the divide-and-conquer process efficiently obtains the local data clusters; an ensemble of diverse trees provides additional robustness. We have conducted classification experiments on cancer images as well as a variety of natural image datasets and the experiment results demonstrate the efficiency and effectiveness of the proposed method.