Color Image Segmentation Research Articles

Image steganalysis: real-time adaptive colour image segmentation for hidden message retrieval and Matthew's correlation coefficient calculation

Image steganalysis: real-time adaptive colour image segmentation for hidden message retrieval and Matthew's correlation coefficient calculation

Multilevel Thresholding Segmentation for Color Image Using Modified Moth-Flame Optimization

Multilevel thresholding has got more attention in the field of image segmentation recently. However, it is still challenging and complicated for color image segmentation in many applications. To mitigate the above conditions, a novel multilevel thresholding algorithm consists of two innovative strategies is proposed on the basis of moth-flame optimization (MFO) to develop the SAMFO-TH algorithm. On one hand, a creative self-adaptive inertia weight scheme is used to enhance both the exploration and exploitation, on the other hand, a newly proposed thresholding (TH) heuristic is embedded into MFO to improve the global performance in multilevel thresholding. To find the optimal threshold values of an image, Otsu's variance, and Kapur's entropy criteria are employed as fitness functions. The experiments have been performed on ten color images including six natural images and four satellite images at different threshold levels with a comparison of other eight meta-heuristic algorithms: multi-verse optimizer (MVO), whale optimization algorithm (WOA), standard MFO, and so on. The experimental results are presented in terms of computational time (CPU time), mean value to reach (MVTR), standard deviation (STD), mean square error (MSE), peak signal to noise ratio (PSNR), structural similarity (SSIM), feature similarity (FSIM), probability rand index (PRI), the variation of information (VoI), and threshold value distortion (TVD). The results demonstrate that the proposed SAMFO-TH outperforms other competitive algorithms and has superiority concerning stability, accuracy, and convergence rate, which can be applied to practical engineering problems.

Multiverse Optimization Algorithm Based on Lévy Flight Improvement for Multithreshold Color Image Segmentation

Multithreshold segmentation is an indispensable part of modern image processing. Color images contain more information than gray images, therefore RGB multi-thresholding segmentation techniques have been drawn much attention during recent years. Multiverse optimization (MVO) algorithm has a strong advantage in finding the optimal solution of three channels for RGB. In this paper, an MVO algorithm based on Levy flight (LMVO) is proposed. Levy flight is an efficient strategy which can not only increase the population diversity to prevent premature convergence but also improve the ability to jump out of the local optimum. Therefore, LMVO conduces to achieve a better balance between exploration and exploitation of MVO, so that it is faster and more robust than MVO and avoids premature convergence. Further LMVO algorithm is compared with the other eight famous meta-heuristics algorithms, by maximizing the objective function of Kapur's entropy method or of Otsu method to determine the optimal threshold. The maximum objective function, peak signal-to-noise ratio (PSNR), feature similarity index (FSIM), structural similarity index (SSIM), CPU calculation time, optimal threshold value, and Wilcoxon's rank-sum test are used to evaluate the quality of the segmented image. The experimental results show that this method has obvious advantages in terms of objective function value, image quality measurement, convergence performance, and robustness.

A Water Cycle Algorithm-Based Multilevel Thresholding System for Color Image Segmentation Using Masi Entropy

In this paper, a recently developed metaheuristic water cycle algorithm (WCA) is coupled with Masi entropy (Masi-WCA) to perform color image segmentation over the optimal threshold value selection process. Masi entropy gives the non-extensive/additive information that exists in an image by a tunable entropic parameter. The water cycle algorithm is a newly established population-based method which has been employed to exploit an optimal value of weighing factors for enforcement of constraints on individual components. The idea behind WCA is grounded on thought of water cycle and how streams and rivers flow downward toward the sea in the real world. The key feature of this paper is to exploit the modern optimization techniques such as water cycle algorithm, monarch butterfly optimization, grasshopper optimization algorithm, bat algorithm, particle swarm optimization, and wind-driven optimization for the color image segmentation purpose. In this paper, two objective (fitness) functions are exploited which are Tsallis and Masi entropy for a fair comparison of the proposed method. The proposed scheme is examined intensively regarding quality, and a statistical graph is included to compare the outcomes of the proposed Masi-WCA method against similar algorithms. Different to other recently developed optimization algorithms used for color image multilevel thresholding operations, WCA presents a better performance in terms of superior quality and fast convergence rate. Experimental evidence encourages the use of WCA for multilevel thresholding with Masi entropy, while it concludes that Tsallis entropy does not outperform over the proposed scheme.

PPK-Means: Achieving Privacy-Preserving Clustering Over Encrypted Multi-Dimensional Cloud Data

Clustering is a fundamental and critical data mining branch that has been widely used in practical applications such as user purchase model analysis, image color segmentation, outlier detection, and so on. With the increasing popularity of cloud computing, more and more encrypted data are converging to cloud computing platforms for enjoying the revolutionary advantages of the cloud computing paradigm, as well as mitigating the deeply concerned data privacy issues. However, traditional data encryption makes existing clustering schemes no more effective, which greatly obstructs effective data utilization and frustrates the wide adoption of cloud computing. In this paper, we focus on solving the clustering problem over encrypted cloud data. In particular, we propose a privacy-preserving k-means clustering technology over encrypted multi-dimensional cloud data by leveraging the scalar-product-preserving encryption primitive, called PPK-means. The proposed technique is able to achieve efficient multi-dimensional data clustering as well to preserve the confidentiality of the outsourced cloud data. To the best of our knowledge, our work is the first to explore the privacy-preserving multi-dimensional data clustering in the cloud computing environment. Extensive experiments in simulation data-sets and real-life data-sets demonstrate that our proposed PPK-means is secure, efficient, and practical.

K-Hyperline Clustering-Based Color Image Segmentation Robust to Illumination Changes

Color image segmentation is very important in the field of image processing as it is commonly used for image semantic recognition, image searching, video surveillance or other applications. Although clustering algorithms have been successfully applied for image segmentation, conventional clustering algorithms such as K-means clustering algorithms are not sufficiently robust to illumination changes, which is common in real-world environments. Motivated by the observation that the RGB value distributions of the same color under different illuminations are located in an identical hyperline, we formulate color classification as a hyperline clustering problem. We then propose a K-hyperline clustering algorithm-based color image segmentation approach. Experiments on both synthetic and real images demonstrate the outstanding performance and robustness of the proposed algorithm as compared to existing clustering algorithms.

A novel beta differential evolution algorithm-based fast multilevel thresholding for color image segmentation

Multilevel thresholding for image segmentation is a crucial process in several applications such as feature extraction and pattern recognition. The meticulous search for the best values for the optimization of fitness function using classical operations needs profuse computational time, which also results in inaccuracy and instability. In this paper, a new beta differential evolution (BDE)-based fast color image multilevel thresholding scheme using two objective functions has been presented. The optimal threshold values are determined by maximizing Kapur’s and Tsallis entropy (entropy criterion) thresholding functions coupled with BDE algorithm. The efficiency of the proposed method is examined over existing multilevel thresholding methods such as artificial bee colony, particle swarm optimization, wind-driven optimization and differential evolution. These approaches are aimed to determine optimum threshold values at different levels of thresholding for color image segmentation. The proficiency of the presented methodology is demonstrated visually and computationally on five real-life true color images as well as four satellite images. Experimental outcomes are exhibited in terms of the optimal threshold value, best objective function and computational cost (in seconds) for each method at different thresholding levels. Afterward, the proposed scheme is examined intensively regarding the superiority of quality. The experimentally evaluated results show that the proposed BDE-based approach for multilevel color image segmentation can accurately and efficiently examine for multiple thresholds, which are near to optimal ones searched using an exhaustive search process.

Approximation of Color Images Based on the Clusterization of the Color Palette and Smoothing Boundaries by Splines and Arcs

The relevance of the results of search, classification, and description of the shapes of objects in images largely depends on the quality of vectorization, i.e., on determining regions that are uniform in color and texture and on constructing their boundaries. A color image segmentation algorithm that clusterizes the color palette of the image by constructing the three-dimensional histogram in the color space HSV is proposed. A feature of this algorithm is that it searches the local maximums on the histogram by scanning the color space with a three-dimensional neighborhood analysis operator. Furthermore, an algorithm for approximating boundaries of regions by line segments and circular arcs that recursively augments the approximated chains is proposed. These algorithms are designed for automating the process of extracting informative features from images for the purpose of using them in computer vision systems, content-based image retrieval systems, geographic information systems and other decision support systems based on graphical information.

Human Pose Estimation in Images and Videos

Estimation of human poses is an interesting and challenging topic in the field of Computer vision. It includes some un-noticed challenges like background effect, the color of the dress, skin tones and many other unpredictable challenges. This is a workable concept because it can be used in sign language recognition, correlating various pose styles from different parts of the world and in medical applications. A deep structure which can represent a man’s body in different models will help in improved recognition of body parts and the spatial correlation between them. For hand detection, features based on hand shape and representation of geometrical details are derived with the help of hand contour. An adaptive and unsupervised approach based on Voronoi region is primarily used for the color image segmentation problem. This process includes identification of key points of the body, which may include body joints and parts. The identification parts will be tough due to small joints and occlusions. Identification of Image features is described in this paper with the help of Box Model Based Estimation, Speed up robust features and finally with Optical flow tracking algorithm. In Optical flow tracking algorithm, we have used Horn-Schunk algorithm to determine featural changes in the images.

Automatic computing of number of clusters for color image segmentation employing fuzzy c-means by extracting chromaticity features of colors

In this paper we introduce a method for color image segmentation by computing automatically the number of clusters the data, pixels, are divided into using fuzzy c-means. In several works the number of clusters is defined by the user. In other ones the number of clusters is computed by obtaining the number of dominant colors, which is determined with unsupervised neural networks (NN) trained with the image’s colors; the number of dominant colors is defined by the number of the most activated neurons. The drawbacks with this approach are as follows: (1) The NN must be trained every time a new image is given and (2) despite employing different color spaces, the intensity data of colors are used, so the undesired effects of non-uniform illumination may affect computing the number of dominant colors. Our proposal consists in processing the images with an unsupervised NN trained previously with chromaticity samples of different colors; the number of the neurons with the highest activation occurrences defines the number of clusters the image is segmented. By training the NN with chromatic data of colors it can be employed to process any image without training it again, and our approach is, to some extent, robust to non-uniform illumination. We perform experiments with the images of the Berkeley segmentation database, using competitive NN and self-organizing maps; we compute and compare the quantitative evaluation of the segmented images obtained with related works using the probabilistic random index and variation of information metrics.

The Effectiveness of Colour Constancy on Segmenting Leukemia Cells Using Unsupervised Clustering Technique

Nowadays, colour image segmentation plays important tasks in medical field application such as brain, eyes, glaucoma and so on. This paper aims to compare the performance between different colour constancy of gray world (GW) and white patch (WP) that will be fusion with clustering algorithm to segment the nucleus of blast cells. First, both of the colour constancy algorithms are utilized on leukemia images to improve the microscopic quality of the blast cells.An unsupervised clustering, namely moving k-means algorithm is utilized on the various colour constancies of GWand WP for the purpose of segmentation. Both colour constancy algorithms have been analysedto identify which one can give the good segmentation performance. Since there is some noise in the segmented nucleus,the combination of median filter and region growing technique plays important tasks inreducing noise. The results show that WP is the best colour constancy in segmenting thenucleus as compared to Gray World.

Color disease leaf image segmentation using NAMS superpixel algorithm

BACKGROUND: Disease leaf segmentation in color image is used to explore the disease shape and lesion regions. It is of great significance for pathological diagnosis and pathological research.OBJECTIVE: This paper proposes a superpixel algorithm using Non-symmetry and Anti-packing Model with Squares (NAMS) for color image segmentation of leaf disease.METHODS:First of all, the NAMS model is presented for color leaf disease image representation. The model can segment images asymmetrically and preserve the characteristics of image context. Second, NAMS based superpixel (NAMS superpixel) algorithm is proposed for clustering pixels, which can represent large homogeneous areas by super squares. By this way, the impact of complex background and the data redundancy in image segmentation can be reduced.RESULTS:Experimental results indicate that compared with segmenting the original image directly and manipulating by Simple Linear Iterative Clustering (SLIC) superpixel, the proposed NAMS superpixel performs more excellently in not only saving storage but also adhering to the lesion region edge.CONCLUSIONS:The outcome of NAMS superpixel can be regarded as a preprocess procedure for leaf disease region detection since the method can segment the image into superpixel blocks and preserve the lesion area.

Block-Matching Fuzzy C-Means clustering algorithm for segmentation of color images degraded with Gaussian noise

In this paper, we present the Block-Matching Fuzzy C-Means (BMFCM) clustering algorithm to segment RGB color images degraded with Additive White Gaussian Noise (AWGN). The contribution of this paper is threefold, namely, noise level estimation, denoising and segmentation. First, two Additive White Gaussian Noise estimation algorithms are proposed to compute the noise variance of the observed noisy color image. Second, we propose an image denoising method based on the enhanced sparse representation using a Block-Matching approach. Third, the Block-Matching Fuzzy C-Means clustering algorithm is proposed. The motivation behind the proposed clustering algorithm is to improve the characteristics of the standard Fuzzy C-Means algorithm, and apply them to segment noisy color images. For this reason, the local information of every color component is incorporated in the Fuzzy C-Means using the proposed Block-Matching based filter as an Additive White Gaussian Noise estimator to determine whether the central pixel in a sliding window is noisy. The presented Additive White Gaussian Noise estimation algorithms are used in the proposed Block-matching method to improve its accuracy. The chromatic subspace of the IJK color space is also applied in the proposed clustering approach providing better segmentation results and reducing the processing time; this is because the algorithm is reduced in a bi-dimensional clustering approach. Finally, visual and numerical experiments demonstrate that the proposed algorithms provide better segmentation results in the presence and absence of AWGN in comparison with other segmentation methods.

Unsupervised color image segmentation with color-alone feature using region growing pulse coupled neural network

Abstract Unsupervised color image segmentation based on low level color features aims to assign same label to all pixels of a region with color homogeneity, which underlies many higher level processing such as object detection and recognition. Pulse coupled neural network (PCNN) is a kind of biologically inspired spiking neural network, and has an inherent image segmentation nature which can combine each pixel's intensity and its spatial relationship with neighboring pixels well. But PCNN cannot deal with color images, which restricts its applications greatly. For the problem, this article studied the color information embedding into PCNN and presented an unsupervised color image segmentation algorithm based on the proposed color region growing PCNN (CRG-PCNN) model. First, RGB color space is converted into Lab in which color distance can be evaluated linearly. Then a linking control unit (LCN) is introduced which essentially is a switch triggered by assessing the color distance among PCNN neuron feeding inputs. When the color distance between two pixels is less than a predefined threshold, a linking between the corresponding neurons is established. By doing so, color information can be embedded into PCNN effectively. Next, the L channel of an input image is fed into CRG-PCNN which can automatically pick out a seed neuron in each iteration and facilitates the region growing continuously by modifying the linking coefficient linearly with the assistance of a fast linking mechanism until certain termination conditions are met. Four widely used quantitative indices for massive experiments conducted on Berkeley segmentation dataset verify the performance of the proposed method. It has good segmentation accuracy and is concise and intuitive.

Building a Globally Optimized Computational Intelligent Image Processing Algorithm for On-Site Inference of Nitrogen in Plants

Estimating nutrient content in plants is a crucial task in the application of precision farming. This work will be more challenging if it is conducted nondestructively based on plant images captured in the field due to the variation of lighting conditions. This paper proposes a computational intelligence image processing to analyze nitrogen status in wheat plants. We developed an ensemble of deep learning multilayer perceptron-using committee machines for color normalization and image segmentation. This paper also focuses on building a genetic-algorithm-based global optimization to fine tune the color normalization and nitrogen estimation results. We discovered that the proposed method can successfully normalize plant images by reducing color variabilities compared to other color normalization techniques. Furthermore, this algorithm is able to enhance the nitrogen estimation results compared to other non-global optimization methods as well as the most renowned SPAD meter based nitrogen measurement.

Image Clustering with Optimization Algorithms and Color Space.

In image clustering, it is desired that pixels assigned in the same class must be the same or similar. In other words, the homogeneity of a cluster must be high. In gray scale image segmentation, the specified goal is achieved by increasing the number of thresholds. However, the determination of multiple thresholds is a typical issue. Moreover, the conventional thresholding algorithms could not be used in color image segmentation. In this study, a new color image clustering algorithm with multilevel thresholding has been presented and, it has been shown how to use the multilevel thresholding techniques for color image clustering. Thus, initially, threshold selection techniques such as the Otsu and Kapur methods were employed for each color channel separately. The objective functions of both approaches have been integrated with the forest optimization algorithm (FOA) and particle swarm optimization (PSO) algorithm. In the next stage, thresholds determined by optimization algorithms were used to divide color space into small cubes or prisms. Each sub-cube or prism created in the color space was evaluated as a cluster. As the volume of prisms affects the homogeneity of the clusters created, multiple thresholds were employed to reduce the sizes of the sub-cubes. The performance of the proposed method was tested with different images. It was observed that the results obtained were more efficient than conventional methods.

Reducing the oversegmentation induced by quasi-flat zones for multivariate images

Quasi-flat zones are morphological operators which partition the image into homogeneous regions with respect to certain criteria. They are used in grayscale and multivariate image simplification and segmentation. However, they often induce an oversegmentation of the image, taking the shape of narrow transition regions between objects and small regions which are a few pixels wide. Various methods have been devised in order to reduce this oversegmentation, which remove the unwanted zones according to some criteria and then grow the remaining regions. In this paper we propose improvements in transition region and area threshold filtering. We also combine the two filtering methods for further-improved results. We apply the proposed approaches in color image segmentation and hyperspectral pixel classification.

Color sensors and their applications based on real-time color image segmentation for cyber physical systems

Color information plays an important role in the color image segmentation and real-time color sensor, which affects the result of video image segmentation and correct real-time temperature value. In this paper, a novel real-time color image segmentation method is proposed, which is based on color similarity in RGB color space. According to the color and luminance information in RGB color space, the dominant color is determined at first, and then color similarity can be calculated with the proposed calculation method of color component, which creates a color-class map. Next, the information of the corresponding color-class map is utilized to classify the pixels. Due to the characteristic that thermal inks feature color values that change in real time as the temperature changes, the segmentation results of thermal ink can be used as a real-time color sensor. Then, we also propose a method of color correction and light source compensation for the sake of potential inaccuracy of its measures. We discuss the proposed segmentation method application combining with color sensor (thermal ink) in real-time color image segmentation for Cyber physical system (CPS) by the application in fire detection and summarize a new method in identifying fire in a video based on these characteristics. The experiments showed that the proposed method in vision-based fire detection and identification in videos was effective; the results were accurate and can be used in real-time analysis.

SVM Pixel Classification on Colour Image Segmentation

The aim of image segmentation is to simplify the representation of an image with the help of cluster pixels into something meaningful to analyze. Segmentation is typically used to locate boundaries and curves in an image, precisely to label every pixel in an image to give each pixel an independent identity. SVM pixel classification on colour image segmentation is the topic highlighted in this paper. It holds useful application in the field of concept based image retrieval, machine vision, medical imaging and object detection. The process is accomplished step by step. At first we need to recognize the type of colour and the texture used as an input to the SVM classifier. These inputs are extracted via local spatial similarity measure model and Steerable filter also known as Gabon Filter. It is then trained by using FCM (Fuzzy C-Means). Both the pixel level information of the image and the ability of the SVM Classifier undergoes some sophisticated algorithm to form the final image. The method has a well developed segmented image and efficiency with respect to increased quality and faster processing of the segmented image compared with the other segmentation methods proposed earlier. One of the latest application result is the Light L16 camera.

A new variational model for joint restoration and segmentation based on the Mumford-Shah model

Due to the typical challenges including image noise or blurriness, intensity inhomogeneity or various image modalities, image segmentation is still an open problem. In this paper, a new variational model is proposed for multiphase segmentation of gray and color images corrupted by noise or blur. Based on the aspects of image restoration, the coupled fidelity terms are utilized in order to effectively and robustly tackle images with a high level of noise or blurriness. For intensity inhomogeneous images, we use the bias-corrected fuzzy c-means method to eliminate the effect of bias field before our implementation. A partial result for the energy minimization problem is established. For solving the new variational model, the alternating minimization algorithm is studied. Experiments demonstrate that our method gives excellent results in terms of segmentation quality in comparison with other state-of-the-art segmentation methods.