Deep Image Segmentation by Quality Inference

• Type: Article in International Conference Proceedings Book
• Date: 2018

Authors

Kelwin Fernandes (Author) Other; Ricardo Cruz (Author) Other; Jaime S. Cardoso (Author) FEUP

Conference proceedings International

Title: Proceedings of the International Joint Conference on Neural Networks

Pages: 1-8

2018 International Joint Conference on Neural Networks, IJCNN 2018

8 July 2018 through 13 July 2018

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 10 Citations

INSPEC

Other information

Authenticus ID: P-00P-SWF

DOI: 10.1109/ijcnn.2018.8489696

Abstract (EN): Traditionally, convolutional neural networks are trained for semantic segmentation by having an image given as input and the segmented mask as output. In this work, we propose a neural network trained by being given an image and mask pair, with the output being the quality of that pairing. The segmentation is then created afterwards through backpropagation on the mask. This allows enriching training with semi-supervised synthetic variations on the ground-truth. The proposed iterative segmentation technique allows improving an existing segmentation or creating one from scratch. We compare the performance of the proposed methodology with state-of-the-art deep architectures for image segmentation and achieve competitive results, being able to improve their segmentations. © 2018 IEEE.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 8