  Image classification is one of the most important areas in computer vision.
Hierarchical multi-label classification applies when a multi-class image
classification problem is arranged into smaller ones based upon a hierarchy or
taxonomy. Thus, hierarchical classification modes generally provide multiple
class predictions on each instance, whereby these are expected to reflect the
structure of image classes as related to one another. In this paper, we propose
a multi-label capsule network (ML-CapsNet) for hierarchical classification. Our
ML-CapsNet predicts multiple image classes based on a hierarchical class-label
tree structure. To this end, we present a loss function that takes into account
the multi-label predictions of the network. As a result, the training approach
for our ML-CapsNet uses a coarse to fine paradigm while maintaining consistency
with the structure in the classification levels in the label-hierarchy. We also
perform experiments using widely available datasets and compare the model with
alternatives elsewhere in the literature. In our experiments, our ML-CapsNet
yields a margin of improvement with respect to these alternative methods.
