  Several works in computer vision have demonstrated the effectiveness of
active learning for adapting the recognition model when new unlabeled data
becomes available. Most of these works consider that labels obtained from the
annotator are correct. However, in a practical scenario, as the quality of the
labels depends on the annotator, some of the labels might be wrong, which
results in degraded recognition performance. In this paper, we address the
problems of i) how a system can identify which of the queried labels are wrong
and ii) how a multi-class active learning system can be adapted to minimize the
negative impact of label noise. Towards solving the problems, we propose a
noisy label filtering based learning approach where the inter-relationship
(context) that is quite common in natural data is utilized to detect the wrong
labels. We construct a graphical representation of the unlabeled data to encode
these relationships and obtain new beliefs on the graph when noisy labels are
available. Comparing the new beliefs with the prior relational information, we
generate a dissimilarity score to detect the incorrect labels and update the
recognition model with correct labels which result in better recognition
performance. This is demonstrated in three different applications: scene
classification, activity classification, and document classification.
