IAM

Check out our CVPR'18 paper on weakly-supervised 3D shape completion — and let me know your opinion! @david_stutz
03rdFEBRUARY2018

READING

Linfeng Xu, Liaoyuan Zeng, Zhengning Wang. Saliency-based superpixels. Signal, Image and Video Processing, 2014.

Xu et al. propose a saliency-based method for generating oversegmentations based on superpixels. However, I want to note that the used notion of oversegmentation (or superpixels) is slightly different in that the authors explicitly want individual objects to be covered by few superpixels. Therefore, the proposed method can also be seen as general segmentation algorithm. Without going into detail, their approach merges superpixels based on saliency. Specifically, the algorithm has similarities to [1] in that edges between pixels (and, thus, between superpixels) are assigned saliency values. Superpixels are then merged only if the created segment increases the overall saliency. Qualitative results are shown in Figure 1.

Figure 1: Qualtiative results in comparison to selected superpixel algorithms and graph-based segmentation [1].

  • [1] P. Felzenswalb and D. Huttenlocher. Efficient graph-based image segmentation. IJCV, 2004.

What is your opinion on the summarized work? Or do you know related work that is of interest? Let me know your thoughts in the comments below or get in touch with me: