Abstract
Several works have shown that relationships between
data points (i.e., context) in structured data can be exploited
to obtain better recognition performance. In this paper, we
explore a different, but related, problem: how can these interrelationships be used to efficiently learn and continuously
update a recognition model, with minimal human labeling
effort. Towards this goal, we propose an active learning
framework to select an optimal subset of data points for manual labeling by exploiting the relationships between them.
We construct a graph from the unlabeled data to represent
the underlying structure, such that each node represents a
data point, and edges represent the inter-relationships between them. Thereafter, considering the flow of beliefs in this
graph, we choose those samples for labeling which minimize
the joint entropy of the nodes of the graph. This results in
significant reduction in manual labeling effort without compromising recognition performance. Our method chooses
non-uniform number of samples from each batch of streaming data depending on its information content. Also, the
submodular property of our objective function makes it computationally efficient to optimize. The proposed framework
is demonstrated in various applications, including document
analysis, scene-object recognition, and activity recognition