DISIR: Deep Image Segmentation with Interactive Refinement

This paper presents an interactive approach for multi-class segmentation of aerial images. Precisely, it is based on a deep neural network which exploits both RGB images and annotations. Starting from an initial output based on the image only, our network then interactively refines this segmentation...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2020-08
Main Authors: Gaston Lenczner, Bertrand Le Saux, Luminari, Nicola, Adrien Chan Hon Tong, Guy Le Besnerais
Format: Article
Language:English
Subjects:
Annotations
Artificial neural networks
Color imagery
Image segmentation
Impact analysis
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper presents an interactive approach for multi-class segmentation of aerial images. Precisely, it is based on a deep neural network which exploits both RGB images and annotations. Starting from an initial output based on the image only, our network then interactively refines this segmentation map using a concatenation of the image and user annotations. Importantly, user annotations modify the inputs of the network - not its weights - enabling a fast and smooth process. Through experiments on two public aerial datasets, we show that user annotations are extremely rewarding: each click corrects roughly 5000 pixels. We analyze the impact of different aspects of our framework such as the representation of the annotations, the volume of training data or the network architecture. Code is available at https://github.com/delair-ai/DISIR.
ISSN:2331-8422
DOI:10.48550/arxiv.2003.14200