Learning multi-level and multi-scale deep representations for privacy image classification

• Published in: Multimedia tools and applications 2022, Vol.81 (2), p.2259-2274
• Main Authors: Han, Yahui, Huang, Yonggang, Pan, Lei, Zheng, Yunbo
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Classification; Computer Communication Networks; Computer Science; Data Structures and Information Theory; Feature extraction; Image classification; Multimedia Information Systems; Privacy; Special Purpose and Application-Based Systems
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-021-11667-5

Privacy image classification can help people detect privacy images when people share images. In this paper, we propose a novel method using multi-level and multi-scale features for privacy image classification. We first use CNN (Convolutional Neural Network) to extract multi-levels features. Then, max-pooling layers are employed to obtain multi-scale features at each level. Finally, we propose two feature aggregation models, called Privacy-MSML and Privacy-MLMS to fuse those features for image privacy classification. In Privacy-MSML, multi-scale features of the same level are first integrated and then the integrated features are fused. In Privacy-MLMS, multi-level features of the same scale are first integrated and then the integrated features are fused. Our experiments on a real-world dataset demonstrate the proposed method can achieve better performance compared with the state-of-the-art solutions.