Learning Superpixels with Segmentation-Aware Affinity Loss

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Authors Wei-Chih Tu, Ming-Yu Liu, +4 authors Jan Kautz
Journal/Conference Name IEEE/CVF Conference on Computer Vision and…
Paper Category
Paper Abstract Superpixel segmentation has been widely used in many computer vision tasks. Existing superpixel algorithms are mainly based on hand-crafted features, which often fail to preserve weak object boundaries. In this work, we leverage deep neural networks to facilitate extracting superpixels from images. We show a simple integration of deep features with existing superpixel algorithms does not result in better performance as these features do not model segmentation. Instead, we propose a segmentation-aware affinity learning approach for superpixel segmentation. Specifically, we propose a new loss function that takes the segmentation error into account for affinity learning. We also develop the Pixel Affinity Net for affinity prediction. Extensive experimental results show that the proposed algorithm based on the learned segmentation-aware loss performs favorably against the state-of-the-art methods. We also demonstrate the use of the learned superpixels in numerous vision applications with consistent improvements.
Date of publication 2018
Code Programming Language Python

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