Information-based objective functions for active data selection
Neural Computation
Selective Sampling Using the Query by Committee Algorithm
Machine Learning
Bayesian Classification With Gaussian Processes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Active + Semi-supervised Learning = Robust Multi-View Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Support Vector Machine Active Learning with Application sto Text Classification
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Employing EM and Pool-Based Active Learning for Text Classification
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
A family of algorithms for approximate bayesian inference
A family of algorithms for approximate bayesian inference
Recognition with Local Features: the Kernel Recipe
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
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
Labeling images with a computer game
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Priors for People Tracking from Small Training Sets
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
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
One-Shot Learning of Object Categories
IEEE Transactions on Pattern Analysis and Machine Intelligence
Peekaboom: a game for locating objects in images
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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
3D People Tracking with Gaussian Process Dynamical Models
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Scalable Recognition with a Vocabulary Tree
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
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
SVM-KNN: Discriminative Nearest Neighbor Classification for Visual Category Recognition
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Appearance-based gender classification with Gaussian processes
Pattern Recognition Letters
Representing shape with a spatial pyramid kernel
Proceedings of the 6th ACM international conference on Image and video retrieval
The Journal of Machine Learning Research
Efficient hyperkernel learning using second-order cone programming
IEEE Transactions on Neural Networks
Efficiently searching for similar images
Communications of the ACM
Learning to recognize objects from unseen modalities
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
One-shot learning of object categories using dependent Gaussian processes
Proceedings of the 32nd DAGM conference on Pattern recognition
Stream-based active unusual event detection
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part I
One-class classification with gaussian processes
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part II
Efficient Gaussian process classification using random decision forests
Pattern Recognition and Image Analysis
Interactive labeling of WCE images
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
Special Issue on Probabilistic Models for Image Understanding, Part II
International Journal of Computer Vision
Robust classification and semi-supervised object localization with Gaussian processes
DAGM'11 Proceedings of the 33rd international conference on Pattern recognition
Large-scale Gaussian process classification using random decision forests
Pattern Recognition and Image Analysis
PLISS: labeling places using online changepoint detection
Autonomous Robots
Large-scale gaussian process classification with flexible adaptive histogram kernels
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
Rapid uncertainty computation with gaussian processes and histogram intersection kernels
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
One-class classification with Gaussian processes
Pattern Recognition
Active learning via neighborhood reconstruction
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
On the generation of a variety of grasps
Robotics and Autonomous Systems
I want to know more--efficient multi-class incremental learning using Gaussian processes
Pattern Recognition and Image Analysis
Self-help: Seeking out perplexing images for ever improving topological mapping
International Journal of Robotics Research
Review: A review of novelty detection
Signal Processing
Active learning for on-road vehicle detection: a comparative study
Machine Vision and Applications
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Discriminative methods for visual object category recognition are typically non-probabilistic, predicting class labels but not directly providing an estimate of uncertainty. Gaussian Processes (GPs) provide a framework for deriving regression techniques with explicit uncertainty models; we show here how Gaussian Processes with covariance functions defined based on a Pyramid Match Kernel (PMK) can be used for probabilistic object category recognition. Our probabilistic formulation provides a principled way to learn hyperparameters, which we utilize to learn an optimal combination of multiple covariance functions. It also offers confidence estimates at test points, and naturally allows for an active learning paradigm in which points are optimally selected for interactive labeling. We show that with an appropriate combination of kernels a significant boost in classification performance is possible. Further, our experiments indicate the utility of active learning with probabilistic predictive models, especially when the amount of training data labels that may be sought for a category is ultimately very small.