Color image segmentation: advances and prospects

Image segmentation is very essential and critical to image processing and pattern recognition. This survey provides a summary of color image segmentation techniques available now. Basically, color segmentation approaches are based on monochrome segmentation approaches operating in different color spaces. Therefore, we first discuss the major segmentation approaches for segmenting monochrome images: histogram thresholding, characteristic feature clustering, edge detection, region-based methods, fuzzy techniques, neural networks, etc.; then review some major color representation methods and their advantages/disadvantages; finally summarize the color image segmentation techniques using different color representations. The usage of color models for image segmentation is also discussed. Some novel approaches such as fuzzy method and physics-based method are investigated as well.     

A novel color image multilevel thresholding based segmentation using nature inspired optimization algorithms

Multilevel thresholding for segmentation is an essential task and indispensable process in various applications. Conventional color multilevel thresholding based image segmentations are computationally expensive, and lack accuracy and stability. To address this issue, this paper introduces the comparative performance study of different objective functions using cuckoo search and other optimization algorithms to solve the color image segmentation problem via multilevel thresholding. During the optimization process, solutions are evaluated using Otsu or Kapur's method. Performance of the proposed approach has been assessed using a variety of benchmark images, and compared against three other nature inspired algorithms namely differential evolution (DE), wind driven optimization (WDO) and particle swam optimization (PSO) algorithms. Results have been analyzed both qualitatively and quantitatively, based on the fitness values of obtained best solutions and four popular performance measures namely PSNR, MSE, SSIM and FSIM indices as well. According to statistical analysis of different nature inspired optimization algorithms, Kapur's entropy was found to be more accurate and robust for multilevel colored satellite image segmentation problem. On the other hand, cuckoo search was found to be most promising for colored satellite image segmentation.     

基于参数型模糊熵的图像分割新方法

直方图阈值法因其简单性和抗噪性在图像处理中得到了广泛应用。针对传统模糊熵阈值法对图像分割最佳阈值选取缺乏鲁棒性的问题,提出了参数型模糊熵图像分割新方法。该方法对图像分割最佳阈值选取具有良好的鲁棒性,适当调整参数可获得满意的视觉分割效果。实验结果表明,提出的方法是可行的。     

基于二维直方图和模糊熵准则的阈值化方法

图像分割是图像分析、识别和理解的基础。图像分割主要是指将图像分成各具特性的区域并提取出感兴趣目标的技术,其研究多年来一直受到人们的高度重视;阈值化法是图像分割的一种重要方法,在图像处理与识别中广为应用;针对图像分割中细节往往被忽略导致后续处理困难的问题,基于模糊关系和最大模糊熵原理提出了一种阈值化方法,对二维直方图进行模糊分割;为了获得图像分割中的细节,提出的方法根据最大熵原则自动确定模糊区域和门限,进而获得二维模糊熵和遗传算法最优解,最后获得图像细节;通过对不同灰度水平和颜色类型图像进行实验比较,实验结果表明提出的方法优于二维非模糊方法和一维模糊熵分割法,得到该方法在图像分割中获得细节的结论。     

基于特征距离的阈值法及其在眼科图象分割中的应用

医学图象的识别与分析能够为临床提供定量比的诊断依据,而图象分割是其中最关键的一步。为提高医学图象侵分割效果,提出了一种基于特征距离的阈值分割算法,并将其与颜色特征分类相结合,来对眼科裂隙灯生物显微镜图象上的角膜充血区进行分割,分割结果可用于角膜充血区的定量体分析,另外,该算法中的样本典型值是通过一种三维直方图分块算法来确定的,实验结果表明,该算法可以有效地分割出角膜充血,其分割效果优于欧氏距离阈值法,且分析数据的精度能够达到临床诊断的要求。     

An optimal color image multilevel thresholding technique using grey-level co-occurrence matrix

Image thresholding is a process that separates particular object within an image from their background. An optimal thresholding technique can be taken as a single objective optimization task, where computation and obtaining a solution can become inefficient, especially at higher threshold levels. In this paper, a new and efficient color image multilevel thresholding approach is presented to perform image segmentation by exploiting the correlation among gray levels. The proposed method incorporates gray-level co-occurrence matrix (GLCM) and cuckoo search (CS) in order to effectively enhance the optimal multilevel thresholding of colored natural and satellite images exhibiting complex background and non-uniformities in illumination and features. The experimental results are presented in terms of mean square error (MSE), peak signal to noise ratio (PSNR), feature similarity index (FSIM), structural similarity index (SSIM), computational time (CPU time in seconds), and optimal threshold values for each primary color component at different thresholding levels for each of the test images. In addition, experiments are also conducted on the Berkeley Segmentation Dataset (BSDS300), and four performance indices of image segmentation- Probability Rand Index (PRI), Variation of Information (VoI), Global Consistency Error (GCE), and Boundary Displacement Error (BDE) are tested. To evaluate the performance of proposed algorithm, other optimization algorithm such as artificial bee colony (ABC), bacterial foraging optimization (BFO), and firefly algorithm (FA) are compared using GLCM as an objective function. Moreover, to show the effectiveness of proposed method, the results are compared to existing context sensitive multilevel segmentation techniques based on Tsalli's entropy. Experimental results showed the superiority of proposed technique in terms of better segmentation results with increased number of thresholds.     

An improved scheme for minimum cross entropy threshold selection based on genetic algorithm

Image segmentation is one of the most critical tasks in image analysis. Thresholding is definitely one of the most popular segmentation approaches. Among thresholding methods, minimum cross entropy thresholding (MCET) has been widely adopted for its simplicity and the measurement accuracy of the threshold. Although MCET is efficient in the case of bilevel thresholding, it encounters expensive computation when involving multilevel thresholding for exhaustive search on multiple thresholds. In this paper, an improved scheme based on genetic algorithm is presented for fastening threshold selection in multilevel MCET. This scheme uses a recursive programming technique to reduce computational complexity of objective function in multilevel MCET. Then, a genetic algorithm is proposed to search several near-optimal multilevel thresholds. Empirically, the multiple thresholds obtained by our scheme are very close to the optimal ones via exhaustive search. The proposed method was evaluated on various types of images, and the experimental results show the efficiency and the feasibility of the proposed method on the real images.     

A multilevel color image segmentation technique based on cuckoo search algorithm and energy curve

Amongst all the multilevel thresholding techniques, standard histogram based thresholding approaches are very impressive for bi-level thresholding. But, it is not effective to select spatial contextual information of the image for choosing optimal thresholds. In this paper, a new color image thresholding technique is presented by using an energy function to generate the energy curve of an image by considering spatial contextual information of the image. The property of this energy curve is very much similar to histogram of the image. To estimate the spatial contextual information for thresholding practice, in place of histogram, the energy curve function is used as an input. A new energy curve based color image segmentation approach using three well known objective functions named Kapur’s entropy, between-class-variance, and Tsalli’s entropy is proposed. In this paper, cuckoo search (CS) and egg lying radius-cuckoo search (ELR-CS) optimization algorithms with different parameter analysis have been used for solving the color image multilevel thresholding problem. The experimental results demonstrate that the proposed CS-Kapur’s energy curve based segmentation can powerfully and accurately search the multilevel thresholds.     

基于谱聚类的多闭值图像分割方法

阈值法是图像分割的一种重要方法,在图像处理与目标识别中广为应用。因此,如何确定阈值是图像分割的关键。提出了一种新的图像阈值分割方法,即通过采用新的相似度函数的谱聚类算法(Dcut)确定图像阈值。采用基于灰度级的权值矩阵代替常用的基于图像像素级的权值矩阵描述图像像素的关系,因而算法需要的存储空间及实现的复杂性与其它基于图的图像分割方法相比大大减少。实验表明,该方法分割图像的时间少,且能够单阈值和多阈值分割图像,与现有的阈值分割方法相比,其具有更为优越的分割性能。     

A modified fuzzy C-means image segmentation algorithm for use with uneven illumination patterns

A novel fuzzy C-mean (FCM) algorithm is proposed for use when active or structured light patterns are projected onto a scene. The underlying inhomogeneous illumination intensity due to the point source nature of the projection, surface orientation and curvature has been estimated and its effect on the object segmentation minimized. Firstly, we modified the recursive FCM algorithm to include biased illumination field estimation. New clustering center and fuzzy clustering functions resulted based on the intensity and average intensity of a pixel neighborhood based object function. Finally, a dilation operator was used on the initial segmented image for further refinement. Experimental results showed the proposed method was effective for segmenting images illuminated by patterns containing underlying biased intensity fields. A higher accuracy was obtained than for traditional FCM and thresholding techniques.     

基于模糊连通性的彩色图像切片序列分割方法

为了对中国虚拟人彩色图像切片序列中人体组织实现分割提取，提出了一种以模糊连通性理论为基础的三维分割方法，结合基于向量的聚类方法和各向异性扩散的局部邻域内数据采样策略，这种方法能够对彩色切片序列中人体组织实现精确定位和分割．本文也将展示一些分割结果和提取组织重建的图例．     

A new social and momentum component adaptive PSO algorithm for image segmentation

In this paper, we present a new variant of Particle Swarm Optimization (PSO) for image segmentation using optimal multi-level thresholding. Some objective functions which are very efficient for bi-level thresholding purpose are not suitable for multi-level thresholding due to the exponential growth of computational complexity. The present paper also proposes an iterative scheme that is practically more suitable for obtaining initial values of candidate multilevel thresholds. This self iterative scheme is proposed to find the suitable number of thresholds that should be used to segment an image. This iterative scheme is based on the well known Otsu’s method, which shows a linear growth of computational complexity. The thresholds resulting from the iterative scheme are taken as initial thresholds and the particles are created randomly around these thresholds, for the proposed PSO variant. The proposed PSO algorithm makes a new contribution in adapting ‘social’ and ‘momentum’ components of the velocity equation for particle move updates. The proposed segmentation method is employed for four benchmark images and the performances obtained outperform results obtained with well known methods, like Gaussian-smoothing method (Lim, Y. K., & Lee, S. U. (1990). On the color image segmentation algorithm based on the thresholding and the fuzzy c-means techniques. Pattern Recognition, 23, 935–952; Tsai, D. M. (1995). A fast thresholding selection procedure for multimodal and unimodal histograms. Pattern Recognition Letters, 16, 653–666), Symmetry-duality method (Yin, P. Y., & Chen, L. H. (1993). New method for multilevel thresholding using the symmetry and duality of the histogram. Journal of Electronics and Imaging, 2, 337–344), GA-based algorithm (Yin, P. -Y. (1999). A fast scheme for optimal thresholding using genetic algorithms. Signal Processing, 72, 85–95) and the basic PSO variant employing linearly decreasing inertia weight factor.     

Segmentation of weather radar image based on hazard severity using RDE: reconstructed mutation strategy for differential evolution algorithm

Weather describes the condition of our atmosphere during a specific period of time, and climate represents a composite of day to day weather over longer period of time. Climatology attempts to analyze and explain the impact of climate so that the society can plan accordingly. Climatology analysis is often done on radar images representing various climatic conditions. These images contain varying scale of severity for any specific climatic parameter of study. The climatologists often find it convenient to analyze climatic conditions if tools are available to segment the weather images based on the severity scale which is represented by different colors. Segmentation of the weather radar image is also used for automated analysis of weather conditions. Differential evolution (DE) approach instead is used for fast selection of optimal threshold. In present paper, we have applied DE with multilevel thresholding for weather image segmentation which results in minimum computational time and excellent image quality. A new mutation strategy for DE named reconstructed differential evolution (RDE) strategy is suggested for better performance over image segmentation. Using fuzzy entropy and RDE for multilevel thresholding provides better results in comparison with last suggested methods.

     

模糊相关图割的非监督层次化彩色图像分割

目的 基于阈值的分割方法能根据像素的信息将图像划分为同类的区域,其中常用的最大模糊相关分割方法,因能利用模糊相关度量划分的适当性,得到较好的分割结果,而广受关注。然而该算法存在划分数需预先确定,阈值的分割结果存在孤立噪声,无法对彩色图像实施分割的问题。为此,提出基于模糊相关图割的非监督层次化分割策略来解决该问题。方法 算法首先将图像划分为若干超像素,以提高层次化图像分割的效率;随后将快速模糊相关算法与图割结合,构成模糊相关图割2-划分算子,在确保分割效率的基础上,解决单一阈值分割存在孤立噪声的问题;最后设计了自顶向下层次化分割策略,利用构建的2-划分算子选择合适的区域及通道,迭代地对超像素实施层次化分割,直到算法收敛,划分数自动确定。结果 对Berkeley分割数据库上300幅图像进行了测试,结果表明算法能有效分割彩色图像,分割精度优于Ncut、JSEG方法,运行时间较这两种方法也提高了近20%。结论 本文算法为最大模糊相关算法在非监督彩色图像分割领域的应用提供指导依据,能用于目标检测和识别领域。     

Multilevel image segmentation with adaptive image context based thresholding

Neural network based image segmentation techniques primarily focus on the selection of appropriate thresholding points in the image feature space. Research initiatives in this direction aim at addressing this problem of effective threshold selection for activation functions. Multilevel activation functions resort to fixed and uniform thresholding mechanisms. These functions assume homogeneity of the image information content. In this paper, we propose a collection of adaptive thresholding approaches to multilevel activation functions. The proposed thresholding mechanisms incorporate the image context information in the thresholding process. Applications of these mechanisms are demonstrated on the segmentation of real life multilevel intensity images using a self-supervised multilayer self-organizing neural network (MLSONN) and a supervised pyramidal neural network (PyraNet).We also present a bi-directional self-organizing neural network (BDSONN) architecture suitable for multilevel image segmentation. The architecture uses an embedded adaptive thresholding mechanism to a characteristic multilevel activation function.The segmentation efficiencies of the thresholding mechanisms evaluated using four unsupervised measures of merit, are reported for the three neural network architectures considered.     

Spatial context cross entropy function based multilevel image segmentation using multi-verse optimizer

Multimedia Tools and Applications - In this paper, a context-sensitive energy curve based cross-entropy method for multilevel color image segmentation is proposed. In thresholding approaches,...     

基于图切割的图像自动分割方法

针对图切割在图像分割中的研究主要集中于交互式图像分割,提出一种基于图切割,对彩色图像和灰度图像均可进行自动分割的方法。通过对图像进行初始化,建立能量函数的数据项、光滑项,图切割求解及迭代执行等步骤自动实现图像的分割,既无需用户人工交互进行约束,也无需对图像建模及对数据分布进行估计,可以较快速度获得良好的分割结果。通过实验对彩色图像和灰度图像进行自动分割,实验结果验证该方法的有效性。     

An adaptive neuro-fuzzy system for automatic image segmentation andedge detection

An autoadaptive neuro-fuzzy segmentation and edge detection architecture is presented. The system consists of a multilayer perceptron (MLP)-like network that performs image segmentation by adaptive thresholding of the input image using labels automatically pre-selected by a fuzzy clustering technique. The proposed architecture is feedforward, but unlike the conventional MLP the learning is unsupervised. The output status of the network is described as a fuzzy set. Fuzzy entropy is used as a measure of the error of the segmentation system as well as a criterion for determining potential edge pixels. The proposed system is capable to perform automatic multilevel segmentation of images, based solely on information contained by the image itself. No a priori assumptions whatsoever are made about the image (type, features, contents, stochastic model, etc.). Such an "universal" algorithm is most useful for applications that are supposed to work with different (and possibly initially unknown) types of images. The proposed system can be readily employed, "as is," or as a basic building block by a more sophisticated and/or application-specific image segmentation algorithm. By monitoring the fuzzy entropy relaxation process, the system is able to detect edge pixels     

Image Thresholding Improved by Global Optimization Methods

Image thresholding is a common segmentation technique with applications in various fields, such as computer vision, pattern recognition, microscopy, remote sensing, and biology. The selection of threshold values for segmenting pixels into foreground and background regions is usually based on subjective assumptions or user judgments under empirical rules or manually determined. This work describes and evaluates six effective threshold selection strategies for image segmentation based on global optimization methods: genetic algorithms, particle swarm, simulated annealing, and pattern search. Experiments are conducted on several images to demonstrate the effectiveness of the proposed methodology.     

C#实现彩色图像分割

针对图像分析应用日益广泛的现状,基于阈值法对彩色图像实现分割进行论述。介绍图像分割基本概念,讲述彩色图像直方图阈值分割方法,利用二维直方图方便地寻找彩色图像阈值,最后通过C#可行的程序稳定、准确地实现彩色图像阈值分割。