Connectivity-constrained Interactive Panoptic Segmentation

12/13/2022
by   Ruobing Shen, et al.
0

We address interactive panoptic annotation, where one segment all object and stuff regions in an image. We investigate two graph-based segmentation algorithms that both enforce connectivity of each region, with a notable class-aware Integer Linear Programming (ILP) formulation that ensures global optimum. Both algorithms can take RGB, or utilize the feature maps from any DCNN, whether trained on the target dataset or not, as input. We then propose an interactive, scribble-based annotation framework.

READ FULL TEXT

page 2

page 7

page 8

research
12/05/2018

Interactive Full Image Segmentation

We address the task of interactive full image annotation, where the goal...
research
01/12/2021

Rethinking Interactive Image Segmentation: Feature Space Annotation

Despite the progress of interactive image segmentation methods, high-qua...
research
06/19/2021

Interactive Object Segmentation with Dynamic Click Transform

In the interactive segmentation, users initially click on the target obj...
research
10/12/2015

Interactive multiclass segmentation using superpixel classification

This paper adresses the problem of interactive multiclass segmentation. ...
research
03/26/2019

Large-scale interactive object segmentation with human annotators

Manually annotating object segmentation masks is very time consuming. In...
research
12/16/2017

An ILP Solver for Multi-label MRFS with Connectivity Constraints

Integer Linear Programming (ILP) formulations of Markov random fields (M...
research
03/05/2021

Fast Interactive Video Object Segmentation with Graph Neural Networks

Pixelwise annotation of image sequences can be very tedious for humans. ...

Please sign up or login with your details

Forgot password? Click here to reset