A Capsule Network for Hierarchical Multi-Label Image Classification

TL;DR

This paper introduces a multi-label capsule network designed for hierarchical image classification, effectively predicting multiple related classes by leveraging the class hierarchy structure.

Contribution

The paper proposes a novel ML-CapsNet model with a specialized loss function for hierarchical multi-label classification, improving upon existing methods.

Findings

Achieves higher accuracy than alternative models

Maintains hierarchical consistency in predictions

Demonstrates effectiveness on standard datasets

Abstract

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.