Improving Multi-label Learning with Missing Labels by Structured Semantic Correlations

TL;DR

This paper introduces a method to improve multi-label learning with missing labels by leveraging structured semantic correlations through a semantic graph, leading to better performance on benchmark datasets.

Contribution

It proposes a novel approach that incorporates structured semantic correlations via a semantic graph Laplacian to address missing labels in multi-label learning.

Findings

Outperforms state-of-the-art methods on four benchmark datasets.

Effective semantic descriptor improves correlation modeling.

Structured semantic correlations enhance multi-label learning accuracy.

Abstract

Multi-label learning has attracted significant interests in computer vision recently, finding applications in many vision tasks such as multiple object recognition and automatic image annotation. Associating multiple labels to a complex image is very difficult, not only due to the intricacy of describing the image, but also because of the incompleteness nature of the observed labels. Existing works on the problem either ignore the label-label and instance-instance correlations or just assume these correlations are linear and unstructured. Considering that semantic correlations between images are actually structured, in this paper we propose to incorporate structured semantic correlations to solve the missing label problem of multi-label learning. Specifically, we project images to the semantic space with an effective semantic descriptor. A semantic graph is then constructed on these images to capture the structured correlations between them. We utilize the semantic graph Laplacian as a smooth term in the multi-label learning formulation to incorporate the structured semantic correlations. Experimental results demonstrate the effectiveness of the proposed semantic descriptor and the usefulness of incorporating the structured semantic correlations. We achieve better results than state-of-the-art multi-label learning methods on four benchmark datasets.