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Semantic segmentation of high-resolution images

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Abstract

Image semantic segmentation is a research topic that has emerged recently. Although existing approaches have achieved satisfactory accuracy, they are limited to handling low-resolution images owing to their large memory consumption. In this paper, we present a semantic segmentation method for high-resolution images. First, we downsample the input image to a lower resolution and then obtain a low-resolution semantic segmentation image using state-of-the-art methods. Next, we use joint bilateral upsampling to upsample the low-resolution solution and obtain a high-resolution semantic segmentation image. To modify joint bilateral upsampling to handle discrete semantic segmentation data, we propose using voting instead of interpolation in filtering computation. Compared to state-of-the-art methods, our method significantly reduces memory cost without reducing result quality.

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Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant No. 61521002), a research grant from the Beijing Higher Institution Engineering Research Center, and the Tsinghua- Tencent Joint Laboratory for Internet Innovation Technology.

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Correspondence to Kun Xu.

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Wang, J., Liu, B. & Xu, K. Semantic segmentation of high-resolution images. Sci. China Inf. Sci. 60, 123101 (2017). https://doi.org/10.1007/s11432-017-9252-5

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Keywords

  • image semantic segmentation
  • high-resolution images
  • joint bilateral upsampling