Communications of the ACM
A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Redundant noisy attributes, attribute errors, and linear-threshold learning using winnow
COLT '91 Proceedings of the fourth annual workshop on Computational learning theory
Learning Boolean Functions in an Infinite Attribute Space
Machine Learning
An introduction to computational learning theory
An introduction to computational learning theory
Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Additive versus exponentiated gradient updates for linear prediction
STOC '95 Proceedings of the twenty-seventh annual ACM symposium on Theory of computing
Support Vector Machines for 3D Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning to resolve natural language ambiguities: a unified approach
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
A Winnow-Based Approach to Context-Sensitive Spelling Correction
Machine Learning - Special issue on natural language learning
Journal of Cognitive Neuroscience
Analytical Image Models and Their Applications
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
A Tale of Two Classifiers: SNoW vs. SVM in Visual Recognition
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Learning Object Representations Using Sequential Patterns
AI '08 Proceedings of the 21st Australasian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Multilinear tensor supervised neighborhood embedding analysis for view-based object recognition
PCM'10 Proceedings of the 11th Pacific Rim conference on Advances in multimedia information processing: Part I
Feature selection by maximum marginal diversity: optimality and implications for visual recognition
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing: Part I
Computer Vision and Image Understanding
Object recognition using discriminative parts
Computer Vision and Image Understanding
A supremum norm based near neighbor search in high dimensional spaces
ICCVG'12 Proceedings of the 2012 international conference on Computer Vision and Graphics
| Hi-index | 0.01 |
This paper describes a novel view-based learning algorithm for 3D object recognition from 2D images using a network of linear units. The SNoW learning architecture is a sparse network of linear functions over a pre-defined or incrementally learned feature space and is specifically tailored for learning in the presence of a very large number of features. We use pixel-based and edge-based representations in large scale object recognition experiments in which the performance of SNoW is compared with that of Support Vector Machines (SVMs) and nearest neighbor using the 100 objects in the Columbia Image Object Database (COIL-100). Experimental results show that the SNoW-based method outperforms the SVM-based system in terms of recognition rate and the computational cost involved in learning. Most importantly, SNoW's performance degrades more gracefully when the training data contains fewer views. The empirical results also provide insight into practical and theoretical considerations on view-based methods for 3D object recognition.