Peipei Dai, Journal of Information Processing Systems Vol. 21, No. 3, pp. 284-295, Jun. 2025  

https://doi.org/10.3745/JIPS.02.0224
Keywords: Attention Mechanism, Convolutional Neural Network, DCRN, Media Images, Multi-label classification
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Abstract

With the development of the information society, the significant increase in images in online environments poses challenges to media image management. To adapt to the development trend of the big data era and improve the classification effect of media images, this study introduces a dense connection refinement network (DRCN) in convolutional neural network image recognition, combined with attention mechanism, to fully utilize image features of different scales and improve the judgment accuracy of object detection based on increasing feature reuse times. The results indicate the consistent loss value of 0.08 for the DRCN-Attention model, while achieving a peak recall rate of 85% after 30 iterations, and a mean average precision exceeding 80%. The classification accuracy of ships reaches 82%, which is 8% higher than the support vector machine model. This indicates that the proposed media image classification method has high classification accuracy, provides a new technical reference for the field of computer vision, and has certain application value in the intelligent management of media images in the era of big data.

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