Automatic Scribble Simulation for Interactive Image Segmentation Evaluation

2016 ◽  
pp. 596-608 ◽  
Author(s):  
Bingjie Jiang ◽  
Tongwei Ren ◽  
Jia Bei
Keyword(s):  
Image Segmentation ◽  
Segmentation Evaluation ◽  
Interactive Image Segmentation ◽  
Image Segmentation Evaluation

Image Segmentation Evaluation in this Century

2011 ◽  
pp. 1812-1817 ◽  
Author(s):  
Yu-Jin Zhang
Keyword(s):  
Image Segmentation ◽  
Video Segmentation ◽  
Empirical Evaluation ◽  
Evaluation Criteria ◽  
Evaluation Methods ◽  
Reference Image ◽  
Empirical Methods ◽  
Segmentation Evaluation ◽  
New Research ◽  
Image Segmentation Evaluation

Image segmentation consists of subdividing an image into its constituent parts and extracting those parts of interest (objects). Due to its importance in image analysis, many research works have been conducted for this process. After 40 years of development, a large number of image (and video) segmentation techniques have been proposed and utilized in various applications (Zhang, 2006). With many algorithms developed, some efforts have been spent also on their evaluation, and these efforts have resulted around 100 evaluation papers that can be found in literature for the last century. Several studies have been made in the past in attempt to characterize these existing evaluation methods (Zhang, 1993; Zhang, 1996; Zhang 2001). Segmentation evaluation methods can be classified into analytical methods and empirical methods (Zhang, 1996). The analysis methods treat the algorithms for segmentation directly by examining the principle of algorithms while the empirical methods judge the segmented image (according to predefined criteria or comparing to reference image) so as to indirectly assess the performance of algorithms. Empirical evaluation is practically more effective and usable than analysis evaluation (Zhang, 1996). Recent advancements for segmentation evaluation are mainly made by the development of empirical evaluation techniques. After providing a list of evaluation criteria and methods proposed in the last century as background, this article will provide a summary of the recent (in 21st century) research works for empirical evaluation of image segmentation. These new research works are classified into three groups: (1) those based on existing techniques, (2) those made with modifications of existing techniques, and (3) those that used dissimilar ideas than that of existing techniques. A comparison of these evaluation methods is made before going to the future trends and conclusion.

scholarly journals Distance Measures for Image Segmentation Evaluation

2006 ◽  
Vol 2006 (1) ◽  
Author(s):  
Xiaoyi Jiang ◽  
Cyril Marti ◽  
Christophe Irniger ◽  
Horst Bunke
Keyword(s):  
Image Segmentation ◽  
Distance Measures ◽  
Segmentation Evaluation ◽  
Image Segmentation Evaluation

A Learning-Based Framework for Supervised and Unsupervised Image Segmentation Evaluation

2014 ◽  
Vol 14 (03) ◽  
pp. 1450014 ◽  
Author(s):  
Jian Lin ◽  
Bo Peng ◽  
Tianrui Li
Keyword(s):  
Image Segmentation ◽  
Ground Truth ◽  
Evaluation Framework ◽  
Segmentation Evaluation ◽  
Machine Learning Methods ◽  
Effectiveness Measure ◽  
Human Evaluation ◽  
Segmentation Quality ◽  
Image Segmentation Evaluation ◽  
Unsupervised Image Segmentation

Image segmentation is a fundamental task in automatic image analysis. However, there is still no generally accepted effectiveness measure which is suitable for evaluating the segmentation quality in every application. In this paper, we propose an evaluation framework which benefits from multiple stand-alone measures. To this end, different segmentation evaluation measures are chosen to evaluate segmentation separately, and the results are effectively combined using machine learning methods. We train and implement this framework in our brand-new segmentation dataset which contains images of different contents with segmentation ground truth and Weizmann segmentation database (WSD). In addition, we provide human evaluation of image segmentation pairs to benchmark the evaluation results of the measures. Experimental results show a better performance than the stand-alone methods.

Review on High Spatial Resolution Remote Sensing Image Segmentation Evaluation

2018 ◽  
Vol 84 (10) ◽  
pp. 629-646 ◽  
Author(s):  
Yangyang Chen ◽  
Dongping Ming ◽  
Lu Zhao ◽  
Beiru Lv ◽  
Keqi Zhou ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Remote Sensing Image ◽  
Segmentation Evaluation ◽  
Image Segmentation Evaluation

Image segmentation evaluation: A survey of unsupervised methods

2008 ◽  
Vol 110 (2) ◽  
pp. 260-280 ◽  
Author(s):  
Hui Zhang ◽  
Jason E. Fritts ◽  
Sally A. Goldman
Keyword(s):  
Image Segmentation ◽  
Segmentation Evaluation ◽  
Unsupervised Methods ◽  
Image Segmentation Evaluation
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