Multi-label Image Classification Algorithm Based on Multi-scale Attention and Graph Model

Abstract

As an important research direction in the field of computer vision, multi-label image classification is widely used in recognition, detection, and other applications.Existing multi-label image classification methods cannot effectively use label correlation information and the corresponding relationship between label semantics and image features, resulting in poor classification ability.A new algorithm for multi-label image classification is proposed.By using tag co-occurrence information and tag prior knowledge to build a graph model, multi-scale attention is used to learn the target in image features, and tag guided attention is used to fuse tag semantic features and image feature information to integrate tag correlation and tag semantic information into model learning.On this basis, a dynamic graph model is constructed based on the graph attention mechanism, and the label information graph model is dynamically updated and learned to integrate the image and label information fully.The experimental results on a multi-label image classification task show that, compared with the existing optimal algorithm, Multi-Label Graph Convolutional Network(MLGCN), the mean Average Precision (mAP) values of the algorithm on the Visual Object Classes-2007(VOC-2007) and Common Object in COntext-2012 (COCO-2012) datasets are improved by 0.6 and 1.2 percentage points, respectively, improving the performance significantly.