Short communication: An evaluation metric for image segmentation of multiple objects

Authors:
Mark Polak;Hong Zhang;Minghong Pi
Affiliations:
Centre for Intelligent Mining Systems, University of Alberta, Edmonton, Alta., Canada T6G 2E8;Centre for Intelligent Mining Systems, University of Alberta, Edmonton, Alta., Canada T6G 2E8;Centre for Intelligent Mining Systems, University of Alberta, Edmonton, Alta., Canada T6G 2E8
Venue:
Image and Vision Computing
Year:
2009

Citing 8
Cited 10

A survey of thresholding techniques

Computer Vision, Graphics, and Image Processing
Performance study of several global thresholding techniques for segmentation

Computer Vision, Graphics, and Image Processing
Three-dimensional image segmentation using a split, merge and group approach

Pattern Recognition Letters
Influence of segmentation over feature measurement

Pattern Recognition Letters
GAMM: genetic algorithms with meta-models for vision

GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Dynamic Measurement of Computer Generated Image Segmentations

IEEE Transactions on Pattern Analysis and Machine Intelligence
An evaluation measure of image segmentation based on object centres

ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part I
Toward a generic evaluation of image segmentation

IEEE Transactions on Image Processing

Ore image segmentation by learning image and shape features

Pattern Recognition Letters
Solidity based local threshold for oil sand image segmentation

ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Adaptive local threshold with shape information and its application to object segmentation

ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
Parametric active membrane for segmentation of multiple objects in an image

Pattern Recognition
Multiple objects segmentation with fuzzy rule-base trained topology adaptive active membrane

Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing
Learning a nonlinear distance metric for supervised region-merging image segmentation

Computer Vision and Image Understanding
How to select microscopy image similarity metrics?

Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine
A stochastic gravitational approach to feature based color image segmentation

Engineering Applications of Artificial Intelligence
A Swarm Intelligence inspired algorithm for contour detection in images

Applied Soft Computing
A new evaluation measure for color image segmentation based on genetic programming approach

Image and Vision Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

It is important to be able to evaluate the performance of image segmentation algorithms objectively. In this paper, we define a new error measure which quantifies the performance of an image segmentation algorithm for identifying multiple objects in an image. This error measure is based on object-by-object comparisons of a segmented image and a ground-truth (reference) image. It takes into account the size, shape, and position of each object. Compared to existing error measures, our proposed error measure works at the object level, and is sensitive to both over-segmentation and under-segmentation. Hence, it can serve as a useful tool for comparing image segmentation algorithms and for tuning the parameters of a segmentation algorithm.