Mutual Information Theory for Adaptive Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Learning of Models for Recognition
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part I
Slightly Supervised Learning of Part-Based Appearance Models
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 6 - Volume 06
Generative versus Discriminative Methods for Object Recognition
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Weakly Supervised Top-down Image Segmentation
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Weakly Supervised Scale-Invariant Learning of Models for Visual Recognition
International Journal of Computer Vision
Feature-based approach to semi-supervised similarity learning
Pattern Recognition
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Weakly supervised learning of part-based spatial models for visual object recognition
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
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This paper addresses the inference of probabilistic classification models using weakly supervised learning. The main contribution of this work is the development of learning methods for training datasets consisting of groups of objects with known relative class priors. This can be regarded as a generalization of the situation addressed by Bishop and Ulusoy (2005), where training information is given as the presence or absence of object classes in each set. Generative and discriminative classification methods are conceived and compared for weakly supervised learning, as well as a non-linear version of the probabilistic discriminative models. The considered models are evaluated on standard datasets and an application to fisheries acoustics is reported. The proposed proportion-based training is demonstrated to outperform model learning based on presence/absence information and the potential of the non-linear discriminative model is shown.