Objective and quantitative segmentation evaluation and comparison
Signal Processing
The quickhull algorithm for convex hulls
ACM Transactions on Mathematical Software (TOMS)
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Evaluating Image Segmentation Algorithms Using the Pareto Front
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Unsupervised Learning of Models for Recognition
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part I
The Pareto Envelope-Based Selection Algorithm for Multi-objective Optimisation
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Image Segmentation Using Local Variation
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
A Pattern Classification Approach to Dynamical Object Detection
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Probabilistic Object Recognition Using Multidimensional Receptive Field Histograms
ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
EdgeFlow: a technique for boundary detection and image segmentation
IEEE Transactions on Image Processing
Evaluating Image Segmentation Algorithms Using the Pareto Front
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
A Method for Objective Edge Detection Evaluation and Detector Parameter Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
State of the art on automatic road extraction for GIS update: a novel classification
Pattern Recognition Letters
Efficient adaptive density estimation per image pixel for the task of background subtraction
Pattern Recognition Letters
Toward Objective Evaluation of Image Segmentation Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image segmentation evaluation: A survey of unsupervised methods
Computer Vision and Image Understanding
Simulation of Ground-Truth Validation Data Via Physically- and Statistically-Based Warps
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
Image segmentation algorithm development using ground truth image data sets
Computer Vision and Image Understanding
A benchmark for 3D mesh segmentation
ACM SIGGRAPH 2009 papers
Partial Similarity of Objects, or How to Compare a Centaur to a Horse
International Journal of Computer Vision
Hi-index | 0.01 |
Image segmentation is the first stage of processing in many practical computer vision systems. While development of particular segmentation algorithms has attracted considerable research interest, relatively little work has been published on the subject of their evaluation. In this paper we propose the use of the Pareto front to allow evaluation and comparison of image segmentation algorithms in multi-dimensional fitness spaces, in a manner somewhat analogous to the use of receiver operating characteristic curves in binary classification problems. The principle advantage of this approach is that it avoids the need to aggregate metrics capturing multiple objectives into a single metric, and thus allows trade-offs between multiple aspects of algorithm behavior to be assessed. This is in contrast to previous approaches which have tended to use a single measure of "goodness", or discrepancy to ground truth data. We define the Pareto front in the context of algorithm evaluation, propose several fitness measures for image segmentation, and use a genetic algorithm for multi-objective optimization to explore the set of algorithms, parameters, and corresponding points in fitness space which lie on the front. Experimental results are presented for six general-purpose image segmentation algorithms, including several which may be considered state-of-the-art.