Robust Multi-View Boosting with Priors

Proceedings European Conference on Computer Vision

Many learning tasks for computer vision problems can be
described by multiple views or multiple features. These views can be
exploited in order to learn from unlabeled data, a.k.a. \multi-view learning".
In these methods, usually the classiers iteratively label each other
a subset of the unlabeled data and ignore the rest. In this work, we propose
a new multi-view boosting algorithm that, unlike other approaches,
specically encodes the uncertainties over the unlabeled samples in terms
of given priors. Instead of ignoring the unlabeled samples during the
training phase of each view, we use the dierent views to provide an aggregated
prior which is then used as a regularization term inside a semisupervised
boosting method. Since we target multi-class applications, we
rst introduce a multi-class boosting algorithm based on maximizing the
mutli-class classication margin. Then, we propose our multi-class semisupervised
boosting algorithm which is able to use priors as a regularization
component over the unlabeled data. Since the priors may contain
a signicant amount of noise, we introduce a new loss function for the
unlabeled regularization which is robust to noisy priors. Experimentally,
we show that the multi-class boosting algorithms achieves state-of-theart
results in machine learning benchmarks. We also show that the new
proposed loss function is more robust compared to other alternatives.
Finally, we demonstrate the advantages of our multi-view boosting approach
for object category recognition and visual object tracking tasks,
compared to other multi-view learning methods.