An Underwater Multi-Label Classification Algorithm Based on a Bilayer Graph Convolution Learning Network with Constrained Codec

Abstract

Within the domain of multi-label classification for micro-videos, utilizing terrestrial datasets as a foundation, researchers have embarked on profound endeavors yielding extraordinary accomplishments. The research into multi-label classification based on underwater micro-video datasets is still in the preliminary stage. There are some challenges: the severe color distortion and visual blurring in underwater visual imaging due to water molecular scattering and absorption, the difficulty in acquiring underwater short video datasets, the sparsity of underwater short video modality features, and the formidable task of achieving high-precision underwater multi-label classification. To address these issues, a bilayer graph convolution learning network based on constrained codec (BGCLN) is established in this paper. Specifically, modality-common representation is constructed to complete the representation of common information and specific information based on the constrained codec network. Then, the attention-driven double-layer graph convolutional network module is designed to mine the correlation information between labels and enhance the modality representation. Finally, the combined modality representation fusion and multi-label classification module are used to obtain the category classifier prediction. In the underwater video multi-label classification dataset (UVMCD), the effectiveness and high classification accuracy of the proposed BGCLN have been proved by numerous experiments.