Recent Advances on Graph-Based Image Segmentation Techniques

2012 ◽  
pp. 140-154
Author(s):  
Chao Zeng ◽  
Wenjing Jia ◽  
Xiangjian He ◽  
Min Xu
Keyword(s):  
Computer Vision ◽  
Image Segmentation ◽  
Minimum Spanning Tree ◽  
Maximum Flow ◽  
Research Area ◽  
Main Concern ◽  
Tree Model ◽  
Segmentation Methods ◽  
Segmentation Algorithms ◽  
Future Work

Image segmentation techniques using graph theory has become a thriving research area in computer vision community in recent years. This chapter mainly focuses on the most up-to-date research achievements in graph-based image segmentation published in top journals and conferences in computer vision community. The representative graph-based image segmentation methods included in this chapter are classified into six categories: minimum-cut/maximum-flow model (called graph-cut in some literatures), random walk model, minimum spanning tree model, normalized cut model and isoperimetric graph partitioning. The basic rationales of these models are presented, and the image segmentation methods based on these graph-based models are discussed as the main concern of this chapter. Several performance evaluation methods for image segmentation are given. Some public databases for testing image segmentation algorithms are introduced and the future work on graph-based image segmentation is discussed at the end of this chapter.

Recent Advances on Graph-Based Image Segmentation Techniques

2013 ◽  
pp. 1323-1337
Author(s):  
Chao Zeng ◽  
Wenjing Jia ◽  
Xiangjian He ◽  
Min Xu
Keyword(s):  
Computer Vision ◽  
Image Segmentation ◽  
Minimum Spanning Tree ◽  
Maximum Flow ◽  
Research Area ◽  
Main Concern ◽  
Tree Model ◽  
Segmentation Methods ◽  
Segmentation Algorithms ◽  
Future Work

Image segmentation techniques using graph theory has become a thriving research area in computer vision community in recent years. This chapter mainly focuses on the most up-to-date research achievements in graph-based image segmentation published in top journals and conferences in computer vision community. The representative graph-based image segmentation methods included in this chapter are classified into six categories: minimum-cut/maximum-flow model (called graph-cut in some literatures), random walk model, minimum spanning tree model, normalized cut model and isoperimetric graph partitioning. The basic rationales of these models are presented, and the image segmentation methods based on these graph-based models are discussed as the main concern of this chapter. Several performance evaluation methods for image segmentation are given. Some public databases for testing image segmentation algorithms are introduced and the future work on graph-based image segmentation is discussed at the end of this chapter.

scholarly journals A NOVEL IMAGE SEGMENTATION APPROACH BASED ON NEUTROSOPHIC SET AND IMPROVED FUZZY C-MEANS ALGORITHM

2011 ◽  
Vol 07 (01) ◽  
pp. 155-171 ◽  
Author(s):  
H. D. CHENG ◽  
YANHUI GUO ◽  
YINGTAO ZHANG
Keyword(s):  
Image Segmentation ◽  
Point Of View ◽  
Neutrosophic Set ◽  
Convergence Criterion ◽  
Complex Objects ◽  
Fuzzy C Means ◽  
Segmentation Methods ◽  
Segmentation Algorithms ◽  
Segmentation Approach ◽  
Fuzzy C Means Algorithm

Image segmentation is an important component in image processing, pattern recognition and computer vision. Many segmentation algorithms have been proposed. However, segmentation methods for both noisy and noise-free images have not been studied in much detail. Neutrosophic set (NS), a part of neutrosophy theory, studies the origin, nature, and scope of neutralities, as well as their interaction with different ideational spectra. However, neutrosophic set needs to be specified and clarified from a technical point of view for a given application or field to demonstrate its usefulness. In this paper, we apply neutrosophic set and define some operations. Neutrosphic set is integrated with an improved fuzzy c-means method and employed for image segmentation. A new operation, α-mean operation, is proposed to reduce the set indeterminacy. An improved fuzzy c-means (IFCM) is proposed based on neutrosophic set. The computation of membership and the convergence criterion of clustering are redefined accordingly. We have conducted experiments on a variety of images. The experimental results demonstrate that the proposed approach can segment images accurately and effectively. Especially, it can segment the clean images and the images having different gray levels and complex objects, which is the most difficult task for image segmentation.

Measures of Image and Video Segmentation

2017 ◽  
pp. 28-53
Author(s):  
Pushpajit A. Khaire ◽  
Roshan R. Kotkondawar
Keyword(s):  
Image Segmentation ◽  
Video Segmentation ◽  
Evaluation Process ◽  
Final Evaluation ◽  
Evaluation Metrics ◽  
Introductory Section ◽  
Segmentation Methods ◽  
Segmentation Algorithms ◽  
Benchmark Datasets ◽  
Traditional Image

Study on Video and Image segmentation is currently limited by the lack of evaluation metrics and benchmark datasets that covers the large variety of sub-problems appearing in image and video segmentation. Proposed chapter provides an analysis of Evaluation Metrics, Datasets for Image and Video Segmentation methods. Importance is on wide-ranging, Datasets robust Metrics which used for evaluation purposes without inducing any bias towards the evaluation results. Introductory Section discusses traditional image and video segmentation methods available, the importance and need of measures, metrics and dataset required to evaluate segmentation algorithms are discussed in next section. Main focus of the chapter explains the measures, metrics and dataset available for evaluation of segmentation techniques of both image and video. The goal is to provide details about a set of impartial datasets and evaluation metrics and to leave the final evaluation of the evaluation process to the understanding of the reader.

scholarly journals Image Segmentation Methods for Flood Monitoring System

Water ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1825
Author(s):  
Nur Muhadi ◽  
Ahmad Abdullah ◽  
Siti Bejo ◽  
Muhammad Mahadi ◽  
Ana Mijic
Keyword(s):  
Computer Vision ◽  
Image Segmentation ◽  
Digital Images ◽  
Rapid Development ◽  
Jaccard Index ◽  
Monitoring Systems ◽  
Dice Similarity Coefficient ◽  
Hybrid Technique ◽  
Segmentation Methods ◽  
Flood Monitoring

Flood disasters are considered annual disasters in Malaysia due to their consistent occurrence. They are among the most dangerous disasters in the country. Lack of data during flood events is the main constraint to improving flood monitoring systems. With the rapid development of information technology, flood monitoring systems using a computer vision approach have gained attention over the last decade. Computer vision requires an image segmentation technique to understand the content of the image and to facilitate analysis. Various segmentation algorithms have been developed to improve results. This paper presents a comparative study of image segmentation techniques used in extracting water information from digital images. The segmentation methods were evaluated visually and statistically. To evaluate the segmentation methods statistically, the dice similarity coefficient and the Jaccard index were calculated to measure the similarity between the segmentation results and the ground truth images. Based on the experimental results, the hybrid technique obtained the highest values among the three methods, yielding an average of 97.70% for the dice score and 95.51% for the Jaccard index. Therefore, we concluded that the hybrid technique is a promising segmentation method compared to the others in extracting water features from digital images.

Research on Threshold Segmentation Algorithms

2013 ◽  
Vol 860-863 ◽  
pp. 2888-2891
Author(s):  
Yu Bing Dong ◽  
Ming Jing Li ◽  
Ying Sun
Keyword(s):  
Pattern Recognition ◽  
Image Segmentation ◽  
Segmentation Method ◽  
Threshold Segmentation ◽  
Segmentation Methods ◽  
Segmentation Algorithms ◽  
Better Than ◽  
Iterative Threshold

Thresholding is one of the critical steps in pattern recognition and has a significant effect on the upcoming steps of image application, the important objectives of thresholding are as follows, and separating objects from background, decreasing the capacity of data consequently increases speed. Various threshold segmentation methods are studied. These methods are compared by using MATLAB7.0. The qualities of image segmentation are elaborated. The results show that iterative threshold segmentation method is better than others.

Unsupervised and Supervised Image Segmentation Using Graph Partitioning

2012 ◽  
pp. 72-94
Author(s):  
Charles-Edmond Bichot
Keyword(s):  
Computer Vision ◽  
Pattern Recognition ◽  
Image Segmentation ◽  
Graph Partitioning ◽  
Research Area ◽  
Important Research ◽  
Pattern Recognition Methods ◽  
Segmentation Approach ◽  
Important Research Area ◽  
High Level

Image segmentation is an important research area in computer vision and its applications in different disciplines, such as medicine, are of great importance. It is often one of the very first steps of computer vision or pattern recognition methods. This is because segmentation helps to locate objects and boundaries into images. The objective of segmenting an image is to partition it into disjoint and homogeneous sets of pixels. When segmenting an image it is natural to try to use graph partitioning, because segmentation and partitioning share the same high-level objective, to partition a set into disjoints subsets. However, when using graph partitioning for segmenting an image, several big questions remain: What is the best way to convert an image into a graph? Or to convert image segmentation objectives into graph partitioning objectives (not to mention what are image segmentation objectives)? What are the best graph partitioning methods and algorithms for segmenting an image? In this chapter, the author tries to answer these questions, both for unsupervised and supervised image segmentation approach, by presenting methods and algorithms and by comparing them.

Research on CCD Infrared Image Threshold Segmentation

2012 ◽  
Vol 220-223 ◽  
pp. 1292-1297
Author(s):  
Xing Ma ◽  
Jun Li Han ◽  
Chang Shun Liu
Keyword(s):  
Image Segmentation ◽  
Automatic Segmentation ◽  
Infrared Image ◽  
Experimental System ◽  
Segmentation Method ◽  
Gray Scale ◽  
Image Detection ◽  
Segmentation Methods ◽  
Segmentation Algorithms ◽  
Characteristics Analysis

In recent years, the gray-scale thresholding segmentation has emerged as a primary tool for image segmentation. However, the application of segmentation algorithms to an image is often disappointing. Based on the characteristics analysis of infrared image, this paper develops several gray-scale thresholding segmentation methods capable of automatic segmentation in regions of pedestrians of infrared image. The approaches of gray-scale thresholding segmentation method are described. Then the experimental system is established by using the infrared CCD device for pedestrian image detection. The image segmentation results generated by the algorithm in the experiment demonstrate that the Otsu thresholding segmentation method has achieved a kind of algorithm on automatic detection and segmentation of infrared image information in regions of interest of image.

scholarly journals AN OVERVIEW OF IMAGE SEGMENTATION ALGORITHMS

2013 ◽  
pp. 150-156
Author(s):  
PUSHPAJIT A. KHAIRE. ◽  
NILESHSINGH V. THAKUR
Keyword(s):  
Image Segmentation ◽  
Ground Truth ◽  
Network Clustering ◽  
Noisy Images ◽  
Segmentation Methods ◽  
Segmentation Algorithms ◽  
Fuzzy Logic Approach ◽  
Logic Approach ◽  
And Performance ◽  
Segmentation Of Images

Image segmentation is a puzzled problem even after four decades of research. Research on image segmentation is currently conducted in three levels. Development of image segmentation methods, evaluation of segmentation algorithms and performance and study of these evaluation methods. Hundreds of techniques have been proposed for segmentation of natural images, noisy images, medical images etc. Currently most of the researchers are evaluating the segmentation algorithms using ground truth evaluation of (Berkeley segmentation database) BSD images. In this paper an overview of various segmentation algorithms is discussed. The discussion is mainly based on the soft computing approaches used for segmentation of images without noise and noisy images and the parameters used for evaluating these algorithms. Some of these techniques used are Markov Random Field (MRF) model, Neural Network, Clustering, Particle Swarm optimization, Fuzzy Logic approach and different combinations of these soft techniques.

scholarly journals Graph Laplacian for Spectral Clustering and Seeded Image Segmentation

2020 ◽  
Author(s):  
Wallace Casaca ◽  
Gabriel Taubin ◽  
Luis Gustavo Nonato
Keyword(s):  
Image Segmentation ◽  
User Interaction ◽  
Graph Laplacian ◽  
Spectral Graph Theory ◽  
Additional Contribution ◽  
Computationally Efficient ◽  
Interactive Segmentation ◽  
Segmentation Methods ◽  
Segmentation Algorithms ◽  
Optimization Schemes

Interactive segmentation methods have gained much attention lately, specially due to their good performance in segmenting complex images and easy utilization. However, most interactive segmentation algorithms rely on sophisticated mathematical formulations whose effectiveness highly depends on the kind of image to be processed. In fact, sharp adherence to the contours of image segments, uniqueness of solution, high computational burden, and extensive user interaction are some of the weaknesses of most existing methods. In this thesis we proposed two novel interactive image segmentation techniques that sort out the issues raised above. The proposed methods rely on Laplace operators, spectral graph theory, and optimization schemes towards enabling highly accurate segmentation tools which demand a reduced amount of user interaction while still being mathematically simple and computationally efficient. The good performance of our segmentation algorithms is attested by a comprehensive set of comparisons against representative state-of-the-art methods. As additional contribution, we have also proposed two new algorithms for inpainting and photo colorization, both of which rely on the accuracy of our segmentation apparatus

scholarly journals A survey of recent interactive image segmentation methods

2020 ◽  
Vol 6 (4) ◽  
pp. 355-384
Author(s):  
Hiba Ramadan ◽  
Chaymae Lachqar ◽  
Hamid Tairi
Keyword(s):  
Computer Vision ◽  
Image Segmentation ◽  
User Interaction ◽  
Initial Step ◽  
Object Extraction ◽  
Evaluation Metrics ◽  
Interactive Image Segmentation ◽  
Segmentation Methods ◽  
Comprehensive Classification

AbstractImage segmentation is one of the most basic tasks in computer vision and remains an initial step of many applications. In this paper, we focus on interactive image segmentation (IIS), often referred to as foreground-background separation or object extraction, guided by user interaction. We provide an overview of the IIS literature by covering more than 150 publications, especially recent works that have not been surveyed before. Moreover, we try to give a comprehensive classification of them according to different viewpoints and present a general and concise comparison of the most recent published works. Furthermore, we survey widely used datasets, evaluation metrics, and available resources in the field of IIS.

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