Graphic image classification method based on an attention mechanism and fusion of multilevel and multiscale deep features

Abstract

The paper explores the issue of privacy breaches caused by sharing and publishing images on social media platforms, which is becoming increasingly serious. While existing deep learning-based methods have achieved remarkable results in private image classification, they only consider single-level and single-scale features and neglect the problem of multilevel and multiscale features. To address this limitation, the paper proposes a novel graphic image classification method that fuses multilevel and multiscale deep features. The proposed method employs a multibranch convolutional neural network to extract multilevel features, which are then processed by a multilevel pooling layer to obtain multiscale features. Additionally, two feature fusion methods based on attention mechanism, Privacy-MSML (Privacy Multi-Scale Multi-Level) and Privacy-MLMS (Privacy Multi-Level Multi-Scale), are designed to fuse the multilevel and multiscale features for image classification. Both methods utilize Bi-LSTM and a self-attention mechanism to capture feature dependencies. The experimental results on public datasets demonstrate that the proposed methods effectively fuse multilevel and multiscale features, leading to a significant improvement in classification performance, which highlights the innovative contribution of the paper in addressing the issue of multilevel and multiscale features in private image classification.