One-Shot Learning of Object Categories
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Sharing Visual Features for Multiclass and Multiview Object Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Representing shape with a spatial pyramid kernel
Proceedings of the 6th ACM international conference on Image and video retrieval
WordNet::Similarity: measuring the relatedness of concepts
HLT-NAACL--Demonstrations '04 Demonstration Papers at HLT-NAACL 2004
Bayesian Online Multitask Learning of Gaussian Processes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Gaussian Processes for Object Categorization
International Journal of Computer Vision
Evaluating Color Descriptors for Object and Scene Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Extensions of the informative vector machine
Proceedings of the First international conference on Deterministic and Statistical Methods in Machine Learning
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 with flexible adaptive histogram kernels
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
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Knowledge transfer from related object categories is a key concept to allow learning with few training examples. We present how to use dependent Gaussian processes for transferring knowledge from a related category in a non-parametric Bayesian way. Our method is able to select this category automatically using efficient model selection techniques. We show how to optionally incorporate semantic similarities obtained from the hierarchical lexical database WordNet [1] into the selection process. The framework is applied to image categorization tasks using state-of-the-art image-based kernel functions. A large scale evaluation shows the benefits of our approach compared to independent learning and a SVM based approach.