Attribute Operators for Color Images: Image Segmentation Improved by the Use of Unsupervised Segmentation Evaluation Methods

2017 ◽  
pp. 249-260
Author(s):  
Sérgio Sousa Filho ◽  
Franklin César Flores
Keyword(s):  
Image Segmentation ◽  
Evaluation Methods ◽  
Color Images ◽  
Unsupervised Segmentation ◽  
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 Unsupervised Segmentation Evaluation Using Area-Weighted Variance and Jeffries-Matusita Distance for Remote Sensing Images

2018 ◽  
Vol 10 (8) ◽  
pp. 1193 ◽  
Author(s):  
Yongji Wang ◽  
Qingwen Qi ◽  
Ying Liu
Keyword(s):  
Distance Measures ◽  
Unsupervised Segmentation ◽  
Segmentation Evaluation ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Segmentation Methods ◽  
Segmentation Quality ◽  
Two Measures ◽  
Multiresolution Segmentation ◽  
Adjacent Segments

Image segmentation is an important process and a prerequisite for object-based image analysis. Thus, evaluating the performance of segmentation algorithms is essential to identify effective segmentation methods and to optimize the scale. In this paper, we propose an unsupervised evaluation (UE) method using the area-weighted variance (WV) and Jeffries-Matusita (JM) distance to compare two image partitions to evaluate segmentation quality. The two measures were calculated based on the local measure criteria, and the JM distance was improved by considering the contribution of the common border between adjacent segments and the area of each segment in the JM distance formula, which makes the heterogeneity measure more effective and objective. Then the two measures were presented as a curve when changing the scale from 8 to 20, which can reflect the segmentation quality in both over- and under-segmentation. Furthermore, the WV and JM distance measures were combined by using three different strategies. The effectiveness of the combined indicators was illustrated through supervised evaluation (SE) methods to clearly reveal the segmentation quality and capture the trade-off between the two measures. In these experiments, the multiresolution segmentation (MRS) method was adopted for evaluation. The proposed UE method was compared with two existing UE methods to further confirm their capabilities. The visual and quantitative SE results demonstrated that the proposed UE method can improve the segmentation quality.

Texture Image Segmentation Based on MRMRF in Contourlet Domain

2012 ◽  
Vol 532-533 ◽  
pp. 732-737
Author(s):  
Xi Jie Wang ◽  
Xiao Fan Zhao
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
Field Model ◽  
Model Parameters ◽  
Texture Image ◽  
Remote Sensing Images ◽  
Unsupervised Segmentation ◽  
Markov Random Field Model ◽  
Markov Random ◽  
Texture Image Segmentation

This paper presents a new multi-resolution Markov random field model in Contourlet domain for unsupervised texture image segmentation. In order to make full use of the merits of Contourlet transformation, we introduce the taditional MRMRF model into Contourlet domain, in a manner of variable interation between two components in the tradtional MRMRF model. Using this method, the new model can automatically estimate model parameters and produce accurate unsupervised segmentation results. The results obtained on synthetic texture images and remote sensing images demonstrate that a better segmentation is achieved by our model than the traditional MRMRF model.

scholarly journals A Fast and Effective Method for Unsupervised Segmentation Evaluation of Remote Sensing Images

2020 ◽  
Vol 12 (18) ◽  
pp. 3005
Author(s):  
Maofan Zhao ◽  
Qingyan Meng ◽  
Linlin Zhang ◽  
Die Hu ◽  
Ying Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Computational Cost ◽  
Visual Interpretation ◽  
Remote Sensing Images ◽  
Unsupervised Segmentation ◽  
Segmentation Evaluation ◽  
Parameters Selection ◽  
Object Based ◽  
Geographic Object ◽  
The Difference

The segmentation of remote sensing images with high spatial resolution is important and fundamental in geographic object-based image analysis (GEOBIA), so evaluating segmentation results without prior knowledge is an essential part in segmentation algorithms comparison, segmentation parameters selection, and optimization. In this study, we proposed a fast and effective unsupervised evaluation (UE) method using the area-weighted variance (WV) as intra-segment homogeneity and the difference to neighbor pixels (DTNP) as inter-segment heterogeneity. Then these two measures were combined into a fast-global score (FGS) to evaluate the segmentation. The effectiveness of DTNP and FGS was demonstrated by visual interpretation as qualitative analysis and supervised evaluation (SE) as quantitative analysis. For this experiment, the ‘‘Multi-resolution Segmentation’’ algorithm in eCognition was adopted in the segmentation and four typical study areas of GF-2 images were used as test data. The effectiveness analysis of DTNP shows that it can keep stability and remain sensitive to both over-segmentation and under-segmentation compared to two existing inter-segment heterogeneity measures. The effectiveness and computational cost analysis of FGS compared with two existing UE methods revealed that FGS can effectively evaluate segmentation results with the lowest computational cost.

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