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IRNet-RS: image retargeting network via relative saliency

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Abstract

Image retargeting (IR) aims to fit different display terminals by changing the image aspect ratio while retaining its important content. The IR methods utilize a saliency map to predict the importance of pixels; however, existing methods assume that multiple objects within the saliency map have the same importance, which does not mimic the human attention mechanism and thus results in inadequate protection of the main content during the retargeting operation. To solve these problems, we propose an image retargeting network via relative saliency (IRNet-RS), which has three key steps: relative saliency detection, edge detection, and IR operation. By using a relative saliency stratified supervision module and saliency rank guidance for the refinement module, IRNet-RS detects the salient objects in an image, allocates different saliency weights to them to obtain a relative saliency map, and then fuses it with the edge detection map to generate an importance map. An adaptive duplicate convolution module is also designed to help the network learn the pixel-by-pixel shift map from the source to the target grid, followed by retargeting the image using pixel shifting. Our experimental results show that the proposed IRNet-RS achieves better performance than alternative methods in terms of saliency rank prediction and retargeting results.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

This work was supported in part by National Natural Science Foundation of China (61806071 and 62102129) and Natural Science Foundation of Hebei Province (F2019202381 and F2019202464).

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Authors and Affiliations

  1. School of Artificial Intelligence, Hebei University of Technology, No.5340, Xiping Road, Tianjin, 300401, China

    Yingchun Guo, Xiaoke Hao & Gang Yan

  2. Purification Equipment Research Institute of CSIC, No.17, Zhanlan Road, Handan, 056027, China

    Meng Zhang

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  1. Yingchun Guo
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Correspondence to Yingchun Guo or Xiaoke Hao.

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Guo, Y., Zhang, M., Hao, X. et al. IRNet-RS: image retargeting network via relative saliency. Neural Comput & Applic 36, 4133–4149 (2024). https://doi.org/10.1007/s00521-023-09258-6

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