Most visual classification tasks assume the authenticity of the label information. However, due to several reasons such as difficulty of annotation or inadvertently due to human error, the annotation can often be noisy. This results in examples that are wrongly annotated. In this paper, we consider the examples that are wrongly annotated to be outliers. The task of learning a robust inlier model in the presence of outliers is typically done through the RANSAC algorithm. In this paper, we show that instead of adopting RANSAC to obtain the `right' model, we could use many instances of randomly sampled sets to build lot of models. The collective decision of all these classifiers can be used to identify samples that are likely to be outliers. This results in a modification to RANSAC SVM to explicitly obtain probable outliers from the set of given samples. Once, the outliers are detected, these examples are excluded from the training set. The method can also be used to identify very hard examples from the training set. In this case, where we believe that the examples are correctly annotated, we can achieve good generalization when such examples are excluded from the training set. The method is evaluated using the standard PASCAL VOC dataset. We show that the method is particularly suited for identifying wrongly annotated examples resulting in improvement of more than 12\% over the RANSAC SVM approach. Hard examples in PASCAL VOC dataset are also identified by this method and in fact this even results in a marginal improvement of the classification accuracy over the base classifier provided with all clean samples.
Recommended citation: Subhabrata Debnath, Anjan Banerjee and Vinay P. Namboodiri, “Adapting RANSAC SVM to detect outliers for Robust Classification”,Proceedings of British Machine Vision Conference (BMVC 2015), Swansea, UK, 2015