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The design of robust classifiers, which can contend with
the noisy and outlier ridden datasets typical of computer vision,
is studied. It is argued that such robustness requires
loss functions that penalize both large positive and negative
margins. The probability elicitation view of classifier design
is adopted, and a set of necessary conditions for the design
of such losses is identified. These conditions are used to derive
a novel robust Bayes-consistent loss, denoted Tangent
loss, and an associated boosting algorithm, denoted TangentBoost.
Experiments with data from the computer vision
problems of scene classification, object tracking, and multiple
instance learning show that TangentBoost consistently
outperforms previous boosting algorithms.
The Discriminant Saliency Tracker (DST) of [Mahadevan and Vasconcelos CVPR-09] maps frames to the feature space where the target is salient compared to the background.
TangentBoost is used to combine the saliency maps in a discriminant manner.
The red tracker uses the robust Tangent loss and the blue tracker uses the Exp loss.
We use the semantic space representation of [N. Rasiwasia and N. Vasconcelos CVPR-09] and
Simply replacing the SVM with the robust TangentBoosted classifier. This results in a
~4% improvement in accuracy. Most improvement is seen in classes with
the most class confusion/outliers such as:
highway, street, tall building
mountain, forest, coast, open country
living room, bedroom, kitchen.
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