An interactive image segmentation algorithm, which accepts user-annotations about a target object and the background, is proposed in this work. We convert user-annotations into interaction maps by measuring distances of each pixel to the annotated locations. Then, we perform the forward pass in a convolutional neural network, which outputs an initial segmentation map. However, the user-annotated locations can be mislabeled in the initial result. Therefore, we develop the backpropagating refinement scheme (BRS), which corrects the mislabeled pixels. Experimental results demonstrate that the proposed algorithm outperforms the conventional algorithms on four challenging datasets. Furthermore, we demonstrate the generality and applicability of BRS in other computer vision tasks, by transforming existing convolutional neural networks into user-interactive ones.
|Title of host publication||Proceedings - 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019|
|Publisher||IEEE Computer Society|
|Number of pages||10|
|Publication status||Published - 2019 Jun|
|Event||32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019 - Long Beach, United States|
Duration: 2019 Jun 16 → 2019 Jun 20
|Name||Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition|
|Conference||32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019|
|Period||19/6/16 → 19/6/20|
Bibliographical notePublisher Copyright:
© 2019 IEEE.
- Grouping and Shape
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition