Color Image Segmentation Method Research Articles

Robust Image Segmentation Using LBP Embedded Region Merging

This paper is under in-depth investigation due to suspicion of possible plagiarism on a high similarity index. The region merging algorithm by the maximal similarity with color histogram has been applied successfully in color image segmentation. However, only using color histogram information is not sufficient and effective for superior segmentation. We propose a novel color image segmentation method based on region merging by using joint color-texture histogram in this paper. The proposed method incorporates both color histogram and texture histogram information to measure the similarity of different regions and thus to guide the region merging process. Experiments show that our method is more accurate and robust than traditional image segmentation methods based on region merging.

Robust color image segmentation using fuzzy c-means with weighted hue and intensity

In this paper we propose a novel method for color image segmentation. This method uses only hue and intensity components (which are chosen rationally) of image and combines those by adaptive tuned weights in a specially defined fuzzy c-means cost function. The tuned weights indicate how informative every color component (hue and intensity) is. Obtaining tuned weights begins with finding peaks of hue and intensity's histograms and continues by obtaining the table of the frequencies of hue and intensity values and computing entropy and contrast of every color component. Also this method specifies proper initial values for cluster centers with the aim of reducing the overall number of iterations and avoiding converging of FCM to wrong centroids. Experimental results demonstrate that our algorithm achieves better segmentation performance and also runs faster than similar methods.

Improved Glowworm Swarm Optimization Algorithm for Multilevel Color Image Thresholding Problem

The thresholding process finds the proper threshold values by optimizing a criterion, which can be considered as a constrained optimization problem. The computation time of traditional thresholding techniques will increase dramatically for multilevel thresholding. To greatly overcome this problem, swarm intelligence algorithm is widely used to search optimal thresholds. In this paper, an improved glowworm swarm optimization (IGSO) algorithm has been presented to find the optimal multilevel thresholds of color image based on the between-class variance and minimum cross entropy (MCE). The proposed methods are examined on standard set of color test images by using various numbers of threshold values. The results are then compared with those of basic glowworm swarm optimization, adaptive particle swarm optimization (APSO), and self-adaptive differential evolution (SaDE). The simulation results show that the proposed method can find the optimal thresholds accurately and efficiently and is an effective multilevel thresholding method for color image segmentation.

슈퍼픽셀의 밀집도 및 텍스처정보를 이용한 DBSCAN기반 칼라영상분할

In this paper, a method of color image segmentation based on DBSCAN(Density Based Spatial Clustering of Applications with Noise) using compactness of superpixels and texture information is presented. The DBSCAN algorithm can generate clusters in large data sets by looking at the local density of data samples, using only two input parameters which called minimum number of data and distance of neighborhood data. Superpixel algorithms group pixels into perceptually meaningful atomic regions, which can be used to replace the rigid structure of the pixel grid. Each superpixel is consist of pixels with similar features such as luminance, color, textures etc. Superpixels are more efficient than pixels in case of large scale image processing. In this paper, superpixels are generated by SLIC(simple linear iterative clustering) as known popular. Superpixel characteristics are described by compactness, uniformity, boundary precision and recall. The compactness is important features to depict superpixel characteristics. Each superpixel is represented by Lab color spaces, compactness and texture information. DBSCAN clustering method applied to these feature spaces to segment a color image. To evaluate the performance of the proposed method, computer simulation is carried out to several outdoor images. The experimental results show that the proposed algorithm can provide good segmentation results on various images.

Predictive efficiency of distinct color image segmentation methods for measuring intramuscular fat in beef

Intramuscular fat (IMF) influences important quality characteristics of meat, such as flavor, juiciness, palatability, odor and tenderness. Thus, the objective of this study was to apply the following image processing techniques to quantify the IMF in beef: palette; sampling, interval of coordinates; black and white threshold; and discriminant function of colors. Thirty-five samples of beef, with a wide range of IMF, were used. Color images were taken of the meat samples from different muscles, with variability in the IMF content. The IMF of a thin cross-section meat was determined by chemical lipid extraction and was predicted by image analysis. The chemical method was compared with the image analysis. The segmentation procedures were validated by the adjustment of a linear regression equation to the series of values that were observed and predicted, as well as the regression parameters evaluated by the F-test. The predictive power of these approaches was also compared by residual analysis and by the decomposition of the mean square deviations. The results showed that the discriminant function was the best color segmentation method to measure intramuscular fat via digital images, but required adjustments in the prediction pattern.

Color image segmentation based on multiobjective artificial bee colony optimization

This paper presents a new color image segmentation method based on a multiobjective optimization algorithm, named improved bee colony algorithm for multi-objective optimization (IBMO). Segmentation is posed as a clustering problem through grouping image features in this approach, which combines IBMO with seeded region growing (SRG). Since feature extraction has a crucial role for image segmentation, the presented method is firstly focused on this manner. The main features of an image: color, texture and gradient magnitudes are measured by using the local homogeneity, Gabor filter and color spaces. Then SRG utilizes the extracted feature vector to classify the pixels spatially. It starts running from centroid points called as seeds. IBMO determines the coordinates of the seed points and similarity difference of each region by optimizing a set of cluster validity indices simultaneously in order to improve the quality of segmentation. Finally, segmentation is completed by merging small and similar regions. The proposed method was applied on several natural images obtained from Berkeley segmentation database. The robustness of the proposed ideas was showed by comparison of hand-labeled and experimentally obtained segmentation results. Besides, it has been seen that the obtained segmentation results have better values than the ones obtained from fuzzy c-means which is one of the most popular methods used in image segmentation, non-dominated sorting genetic algorithm II which is a state-of-the-art algorithm, and non-dominated sorted PSO which is an adapted algorithm of PSO for multi-objective optimization.

Color Image Segmentation By Cuckoo Search

In this paper, a clustering based color image segmentation technique is proposed and the clustering technique is optimized by the cuckoo search method. The proposed approach consists of two phase segmentation processes. In the first phase, cluster centres are optimized by using the cuckoo search algorithm and in the second phase, empty and frequent clutters are removed and merged according to pre-defined rules. This cluster centre based clustering technique is then used to find the optimum centre within a cluster, while cuckoo search is applied to find the optimum cluster centre for each segment in the image. Comparison of the proposed method is performed with the genetic algorithm (GA), dynamic control particle swarm optimization (DCPSO) algorithm and firefly algorithm based color image segmentation methods over five benchmark color images. The parameters of the proposed method are tuned through empirical testing. Results demonstrated that the proposed method can be an effective tool for image segmentation.

Genetic algorithm and self organizing map based fuzzy hybrid intelligent method for color image segmentation

The grouping of pixels based on some similarity criteria is called image segmentation. In this paper the problem of color image segmentation is considered as a clustering problem and a fixed length genetic algorithm (GA) is used to handle it. The effectiveness of GA depends on the objective function (fitness function) and the initialization of the population. A new objective function is proposed to evaluate the quality of the segmentation and the fitness of a chromosome. In fixed length genetic algorithm the chromosomes have same length, which is normally set by the user. Here, a self organizing map (SOM) is used to determine the number of segments in order to set the length of a chromosome automatically. An opposition based strategy is adopted for the initialization of the population in order to diversify the search process. In some cases the proposed method makes the small regions of an image as separate segments, which leads to noisy segmentation. A simple ad hoc mechanism is devised to refine the noisy segmentation. The qualitative and quantitative results show that the proposed method performs better than the state-of-the-art methods.

Image Segmentation Utilizing Color-Space Feature

In this paper, the authors introduced a stochastic model for color images. Utilizing this model, they proposed a new method for color image segmentation. The proposed method consists of three stages; the first stage considers the red, green, and blue color component of the image as a gray image. One of the known gray image Thresholding algorithm is applied on the three color components. The second stage segments the image based on the results of first stage. This stage produces eight color segments. The third stage identifies the segments through color-space correlation. Color-space correlation algorithm assumes that a set of pixels are considered to belong to one region if and only if they belong to the same color cluster and all connected using neighborhood filters. The last stage may produce very small segments. These small segments are merged with their closed neighbors based on color features. Finally, Conducted experiments achieved perceptually accepted segments and compare favorably to other segmentation methods.

Color Image Segmentation Using Adaptive GrowCut Method

An adaptive algorithm for interactive multi-label segmentation of 2-dimensional color images using GrowCut has been carry out in this technique. Except certain small number of user-labelled pixels, the remainder of the image is segmented automatically by a layered automaton. This technique is iterative, since the mechanism labels the image automatically. The segmentation task is complex to compute. User can monitor the progress of segmentation and redirect the algorithm with respect to human participation. The proposed system contains two phases. During the first level the original input color image is segmented using the GrowCut method. In the following phase the resulting image is processed under color image segmentation method. In the areas, where the segmentation is dependably computed automatically no further user effort is necessary. Outcomes of segmenting standard photos and images are presented. Our experiments show that a little user effort is required for segmentation of moderately tough images.

Color image segmentation based on intensity and hue clustering - a comparison of LS and LAD approaches

Motivated by the method for color image segmentation based on intensity and hue clustering proposed in [26] we give some theoretical explanations for this method that directly follows from the natural connection between the maximum likelihood approach and Least Squares or Least Absolute Deviations clustering optimality criteria. The method is tested and illustrated on a few typical situations, such as the presence of outliers among the data.

Color Image Segmentation Method Based on Improved Spectral Clustering Algorithm

Contraposing to the features of image data with high sparsity of and the problems on determination of clustering numbers, we try to put forward an color image segmentation algorithm, combined with semi-supervised machine learning technology and spectral graph theory. By the research of related theories and methods of spectral clustering algorithms, we introduce information entropy conception to design a method which can automatically optimize the scale parameter value. So it avoids the unstability in clustering result of the scale parameter input manually. In addition, we try to excavate available priori information existing in large number of non-generic data and apply semi-supervised algorithm to improve the clustering performance for rare class. We also use added tag data to compute similar matrix and perform clustering through FKCM algorithms. By the simulation of standard dataset and image segmentation, the experiments demonstrate our algorithm has overcome the defects of traditional spectral clustering methods, which are sensitive to outliers and easy to fall into local optimum, and also poor in the convergence rate

Interactive color image segmentation via iterative evidential labeling

We develop an interactive color image segmentation method in this paper. This method makes use of the conception of Markov random fields (MRFs) and D–S evidence theory to obtain segmentation results by considering both likelihood information and priori information under Bayesian framework. The method first uses expectation maximization (EM) algorithm to estimate the parameter of the user input regions, and the Bayesian information criterion (BIC) is used for model selection. Then the beliefs of each pixel are assigned by a predefined scheme. The result is obtained by iteratively fusion of the pixel likelihood information and the pixel contextual information until convergence. The method is initially designed for two-label segmentation, however it can be easily generalized to multi-label segmentation. Experimental results show that the proposed method is comparable to other prevalent interactive image segmentation algorithms in most cases of two-label segmentation task, both qualitatively and quantitatively.

COLOR SEGMENTATION OF MGG COLORED CYTOLOGICAL IMAGES USING NON LINEAR OPPONENT COLOR SPACES

This paper presents a color image segmentation method for the quantification of viable cells from samples obtained after cytocentrifugation process and May Grunwald Giemsa (MGG) coloration and then observed by optical microscopy. The method is based on color multi-thresholding and mathematical morphology processing using color information on human visual system based models such as CIELAB model, LUX (Logarithmic hUe eXtension) model and CoLIP (Color Logarithmic Image Processing) model, a new human color vision based model also presented in this article. The results show that the CoLIP model, developed following each step of the human visual color perception, is particularly well adapted for this type of images.

A simple method of rapid and automatic color image segmentation for serialized Visible Human slices

In this paper, a rapid and automatic color image segmentation method for the serialized slices of the Visible Human is proposed. The main strategy is based on region growing and pixel color difference. A rapid color similarity computing method is improved and applied for classifying different pixels. An algorithm based on corrosion from four directions is proposed to automatically extract the seed points for the serialized slices. Utilizing this method, the color slice images of the Visible Human body can be segmented in series automatically. Also, the multithreading frame of parallel computing is introduced in the entire segmentation process. This method is simple but rapid and automatic. The primary organs of the Visible Human can be segmented clearly and accurately. The 3D models of these organs after 3D reconstruction are satisfactory. This novel method can provide support to the Visible Human research.

Image Segmentation using Weighted Average Local Histogram

The prime objective of this paper is to implement an efficient improved color image segmentation method using local histogram and region merging technique. The goal of image segmentation is to cluster pixels into salient image regions, i.e. regions corresponding to individual surfaces, objects or natural parts of objects. Segmentation can be used for object recognition, occlusion boundary estimation within motion or stereo systems, image compression, image editing or image database look-up. Usually image segmentation is an initial and vital step in a series of processes aimed at overall image understanding. There are various techniques for image segmentation. In this research paper, a thorough work has done on the average local histogram of three different color spaces RGB, HSV & Lab. After that k-means clustering and labeling have done on the image for final segmentation. General Terms Image Segmentation, Weighted Average Local Histogram.

An Integrated approach for color Image Segmentation using edge and color information

Image segmentation is a fundamental and complex task in many image processing applications. In this paper, we propose a hybrid method for color image segmentation by integrating color edge information and seeded region growing. We use the color edge information to decide the best position to place the seed and to control the growth of the region. By using color edge detection technique, regions without edges are labeled as the initial seed. Regions are grown from this seed point by adding neighboring pixels that are similar based on homogeneity criterion. The obtained edge map and the region growth map are fused together to produce homogeneous regions with closed boundaries. Finally region merging is done to merge small regions.

Saliency-based color image segmentation in foreign fiber detection

It is difficult to separate foreign fiber objects from the background in a live image captured by an automated visual inspection system for foreign fiber detection due to the inhomogeneous background brightness and various types of foreign fibers in different colors and shapes. This paper presents a saliency-based color image segmentation method aiming at foreign fiber detection. The RGB color image captured in real-time was firstly separated into R, G and B color channels. Then the red, green and blue color features were calculated respectively from the corresponding R, G and B channels. Afterwards, three saliency maps were obtained from these three color features and then fused together. The fused saliency map was segmented to get the color foreign fiber objects. Those foreign fiber objects in dark black or bright white were also segmented out using a threshold method from the brightness saliency map. Finally, all foreign fiber objects obtained were fused together to obtain the final objects. The results indicate that the proposed method can segment out color foreign fiber objects as well as gray foreign fiber objects in dark black or bright white.

A modified semi-supervised color image segmentation method

The paper proposed a modified color image segmentation method basing on semi-supervised hidden Markov random fields (HMRF) with constraints. Making use of MeanShift algorithm to get supervision information and, cluster number and initial values for cluster centroids, color images can be segmented effectively with the method in this paper by K-Means algorithm. The experimental results are very encouraging.

슈퍼픽셀과 FCM을 이용한 클러스터 초기값 설정 및 칼라영상분할

본 논문에서는 슈퍼픽셀과 FCM을 이용한 클러스터 초기값 설정방법과 이를 사용한 칼라영상분할을 연구한다. 클러스터링을 이용한 대표적인 칼라영상분할 방법으로 Fuzzy C-menas (FCM) 알고리즘을 많이 사용한다. FCM은 하나의 데이터가 각 클러스터에 서로 다른 소속도를 갖도록 한다. 그러나 FCM은 초기값 설정에 따라 국부적인 수렴문제가 발생한다. 따라서 초기값 설정문제는 매우 중요한데 본 연구에서는 슈퍼픽셀을 이용하여 클러스터의 초기값을 구하는 방법을 제안한다. 슈퍼픽셀은 원 영상에서 특성이 비슷한 화소들의 묶음으로 표현되는데 먼저 원 영상으로부터 슈퍼픽셀을 구하고 이를 <TEX>$La^*b^*$</TEX> 칼라특징공간에 투영하여 클러스터 초기값을 구한다. 제안방법에서 슈퍼픽셀의 수는 원영상의 화소 수보다 일반적으로 매우 적어서 클러스터 초기값 설정을 위한 고속처리가 가능하다. 제안된 알고리즘의 성능평가를 위해 다양한 칼라영상을 사용하여 컴퓨터 모의실험을 수행하였으며 실험결과 제안방법이 기존방법에 비해 영상분할 성능이 우수함을 알 수 있었다. In this paper, a setting method of initial cluster centers and color image segmentation using superpixels and Fuzzy C-means(FCM) algorithm is proposed. Generally, the FCM can be widely used to segment color images, and an element is assigned to any cluster with each membership values in the FCM. However the algorithm has a problem of local convergence by determining the initial cluster centers. So the selection of initial cluster centers is very important, we proposed an effective method to determine the initial cluster centers using superpixels. The superpixels can be obtained by grouping of some pixels having similar characteristics from original image, and it is projected <TEX>$La^*b^*$</TEX> feature space to obtain the initial cluster centers. The proposed method can be speeded up because number of superpixels are extremely smaller than pixels of original image. To evaluate the proposed method, several color images are used for computer simulation, and we know that the proposed method is superior to the conventional algorithm by the experimental results.