  Contextual information in human environments, such as signs, symbols, and
objects provide important information for robots to use for exploration and
navigation. To identify and segment contextual information from complex images
obtained in these environments, data-driven methods such as Convolutional
Neural Networks (CNNs) are used. However, these methods require large amounts
of human labeled data which are slow and time-consuming to obtain. Weakly
supervised methods address this limitation by generating pseudo segmentation
labels (PSLs). In this paper, we present the novel Weakly Supervised Mask Data
Distillation (WeSuperMaDD) architecture for autonomously generating PSLs using
CNNs not specifically trained for the task of context segmentation; i.e., CNNs
trained for object classification, image captioning, etc. WeSuperMaDD uniquely
generates PSLs using learned image features from sparse and limited diversity
data; common in robot navigation tasks in human-centred environments (malls,
grocery stores). Our proposed architecture uses a new mask refinement system
which automatically searches for the PSL with the fewest foreground pixels that
satisfies cost constraints. This removes the need for handcrafted heuristic
rules. Extensive experiments successfully validated the performance of
WeSuperMaDD in generating PSLs for datasets with text of various scales, fonts,
and perspectives in multiple indoor/outdoor environments. A comparison with
Naive, GrabCut, and Pyramid methods found a significant improvement in label
and segmentation quality. Moreover, a context segmentation CNN trained using
the WeSuperMaDD architecture achieved measurable improvements in accuracy
compared to one trained with Naive PSLs. Our method also had comparable
performance to existing state-of-the-art text detection and segmentation
methods on real datasets without requiring segmentation labels for training.
