AG 1, AG 2, AG 3, AG 4, AG 5, RG1, SWS, MMCI   Info

Public Audience

English

60 Minutes

Saarbrücken

Object class recognition is an active topic in computer vision still presenting many challenges. In most approaches, this task is addressed by supervised learning algorithms that need a large quantity of labels to perform well. This leads either to small datasets (< 10,000 images) that capture only a subset of the real-world class distribution (but with a controlled and verified labeling procedure), or to large datasets that are more representative but also add more label noise. Therefore, semi-supervised learning is a promising direction. It requires only few labels while simultaneously making use of the vast amount of images available today. In this thesis, we address object class recognition with semi-supervised learning. These algorithms depend on the underlying structure given by the data, the image description, and the similarity measure, and the quality of the labels. In the talk, I will illustrate this strong relationship between classification performance and the quality of the labels as well as the structure. Furthermore, I will show how we tackle these problems by active learning to get more representative labels and by several graph improvements.

Cornelia Balzert

9325-2000

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