This paper introduces an approach for handling complex labelling problems driven by local constraints. The purpose is illustrated by two applications: detection of the road networ...
In many vision problems, instead of having fully labeled training data, it is easier to obtain the input in small groups, where the data in each group is constrained to be from th...
We consider a variation of the problem of combining expert opinions for the situation in which there is no ground truth to use for training. Even though we don’t have labeled da...
Sharath R. Cholleti, Sally A. Goldman, Avrim Blum,...
The performance of image retrieval with SVM active learning is known to be poor when started with few labelled images only. In this paper, the problem is solved by incorporating t...
In this work we propose a hierarchical approach for labeling semantic objects and regions in scenes. Our approach is reminiscent of early vision literature in that we use a decompo...