Color Image Segmentation Research Articles

Multilevel Thresholding of Color Image Segmentation Using Memory-based Grey Wolf Optimizer With Otsu Method, Kapur, and M.Masi Entropy

Determining the optimal threshold value for image segmentation has become more attention in recent years because of its varied uses. Otsu-based thresholding methods, minimum cross entropy, and Kapur entropy are efficient for solving bi-level thresholding image segmentation problems (BL-ISP), but not with multi-level thresholding image segmentation problems (ML-ISP). The main problem is exponentially increasing computational complexity. This study uses the memory-based Gray Wolf Optimizer (mGWO) to determine the optimal threshold value for solving ML-ISP on RGB images. The mGWO method is a variant of the standard grey wolf optimizer (GWO) that utilizes the best track record of each individual grey wolf for the global exploration and local exploitation phases of the problem solution space. The solution candidates are represented by each grey wolf using the image intensity values and optimized according to mGWO characteristics. Three objective functions, namely the Otsu method, Kapur Entropy, and M.Masi Entropy are used to evaluate the solutions generated in the optimization process. The GridSearch method is used to determine the optimal parameter combination of each method based on 10 training images. Evaluation of the performance of the mGWO method was measured using several benchmark images and compared with five standard swarm intelligence (SI) methods as benchmarks. Analysis of the results was carried out qualitatively and quantitatively based on the average PSNR, RMSE, SSIM, UQI, fitness value, and CPU processing time from 30 tests. The results were analyzed further with the Wilcoxon signed-rank test. The experimental results show that the performance of the mGWO method outperforms the benchmark method in most experiments and metrics. The mGWO variant also proved to be superior to the standard GWO in resolving multi-level color image segmentation problems. The mGWO performance results are also compared with other state-of-the-art SI methods in solving ML-ISP on grayscale images and was able to outperform those methods in most experiments.

Full-parameter adaptive fuzzy clustering for noise image segmentation based on non-local and local spatial information

The fuzzy clustering C-means (FCM) algorithm is a compelling image segmentation method in image segmentation. However, the algorithm is less robust for images containing noise. This paper proposes a non-local full-parameter adaptive spatial information combined with a local fuzzy factor for noisy image segmentation. Firstly, we realize the adaptive calculation of the smoothing parameter, search term window, and neighborhood window in the non-local spatial information by defining the smoothness and designing the adaptive matching function. Secondly, the image’s non-local and local spatial information is considered comprehensively to reduce noise interference and segmentation ambiguity. Finally, the weighted average membership linking is used as the denominator of the objective function to reduce the number of iterations. The results in synthetic noise image and color image segmentation experiments show that the proposed algorithm has outstanding performance in various evaluation metrics and visual effects, outperforming most other variants of fuzzy clustering-based algorithms.

Symmetric cross-entropy multi-threshold color image segmentation based on improved pelican optimization algorithm.

To address the problems of low accuracy and slow convergence of traditional multilevel image segmentation methods, a symmetric cross-entropy multilevel thresholding image segmentation method (MSIPOA) with multi-strategy improved pelican optimization algorithm is proposed for global optimization and image segmentation tasks. First, Sine chaotic mapping is used to improve the quality and distribution uniformity of the initial population. A spiral search mechanism incorporating a sine cosine optimization algorithm improves the algorithm's search diversity, local pioneering ability, and convergence accuracy. A levy flight strategy further improves the algorithm's ability to jump out of local minima. In this paper, 12 benchmark test functions and 8 other newer swarm intelligence algorithms are compared in terms of convergence speed and convergence accuracy to evaluate the performance of the MSIPOA algorithm. By non-parametric statistical analysis, MSIPOA shows a greater superiority over other optimization algorithms. The MSIPOA algorithm is then experimented with symmetric cross-entropy multilevel threshold image segmentation, and eight images from BSDS300 are selected as the test set to evaluate MSIPOA. According to different performance metrics and Fridman test, MSIPOA algorithm outperforms similar algorithms in global optimization and image segmentation, and the symmetric cross entropy of MSIPOA algorithm for multilevel thresholding image segmentation method can be effectively applied to multilevel thresholding image segmentation tasks.

Multi-threshold segmentation of grayscale and color images based on Kapur entropy by bald eagle search optimization algorithm with horizontal crossover and vertical crossover

Multi-threshold segmentation of grayscale and color images based on Kapur entropy by bald eagle search optimization algorithm with horizontal crossover and vertical crossover

YOLO Based Deep Learning Model for Segmenting the Color Images

The first stage is to extract fine details from a picture using Red Green Blue (RGB) colour space is colour image segmentation. Most grayscale and colour picture segmentation algorithms use original or updated fuzzy c-means (FCM) clustering. However, due to two factors, the majority of these methods are inefficient and fail to produce the acceptable segmentation results for colour photos. The inclusion of local spatial information often results in a high level of computational complexity due to the repetitive distance computation between clustering centres and pixels within a tiny adjacent window. The second reason is that a typical neighbouring window tends to mess up the local spatial structure of images. Color picture segmentation has been improved by introducing Deep Convolution Neural Networks (CNNs) for object detection, classification and semantic segmentation. This study seeks to build a light-weight for object detector that uses a depth and colour image from a publically available dataset to identify objects in a scene. It's likely to output in the depth way by expanding the YOLO network's network architecture. Using Taylor based Cat Salp Swarm algorithm (TCSSA), the weight of the suggested model is modified to improve the accuracy of region extraction findings. It is possible to test the detector's efficacy by comparing it to various datasets. Testing showed that the suggested model is capable of segmenting input into multiple metrics using bounding boxes. The results shows that the proposed model achieved 0.20 of Global Consistency Error (GCE) and 1.85 of Variation of Information (VOI) on BSDS500 dataset, where existing techniques achieved nearly 1.96 to 1.86 of VOI and 0.25 to 0.22 of GCE for the same dataset.

Giza pyramids construction algorithm with gradient contour approach for multilevel thresholding color image segmentation

Giza pyramids construction algorithm with gradient contour approach for multilevel thresholding color image segmentation

A reliable region information driven kriging-assisted multiobjective rough fuzzy clustering algorithm for color image segmentation

Multiobjective clustering algorithms (MOCAs) are becoming increasingly popular with the merit of segmenting images from multiple perspectives. The performances of MOCAs highly depend on their fitness functions. However, most existing MOCAs adopt one pair of complementary fitness functions, which always measure intra-class compactness and inter-class separation, respectively. This may result in insufficient ability to recognize and mine feature structures from complex images. Moreover, information within color images, such as region information and uncertain information, can barely receive enough attention in MOCAs. To resolve these problems, we propose a reliable region information driven Kriging-assisted multiobjective rough fuzzy clustering algorithm (RRI-KMRFC). Firstly, a reliability-based region information extraction strategy (RRIES) is designed to obtain reliable image information with satisfactory regional homogeneity and abundant image details. Secondly, the derived region information is used to construct three complementary fitness functions, focusing on rough intra-class compactness, rough inter-class separation, and regional consistency, respectively. Such fitness functions can effectively identify the clustering structure, maintain contour details, and characterize uncertain information from color images. To efficiently optimize the proposed functions, an incremental Kriging-assisted evolutionary framework is presented to decrease the expensive function evaluations in which an improved infill sampling strategy is devised to assist in finding unexplored areas in the decision space. Finally, a rough fuzzy clustering validity index with reliable region information is proposed to select the optimal trade-off solution. Experiments performed on Berkeley and Weizmann images confirm the effectiveness and robustness of RRI-KMRFC.

Adaptive Density Spatial Clustering Method Fusing Chameleon Swarm Algorithm.

The density-based spatial clustering of application with noise (DBSCAN) algorithm is able to cluster arbitrarily structured datasets. However, the clustering result of this algorithm is exceptionally sensitive to the neighborhood radius (Eps) and noise points, and it is hard to obtain the best result quickly and accurately with it. To solve the above problems, we propose an adaptive DBSCAN method based on the chameleon swarm algorithm (CSA-DBSCAN). First, we take the clustering evaluation index of the DBSCNA algorithm as the objective function and use the chameleon swarm algorithm (CSA) to iteratively optimize the evaluation index value of the DBSCAN algorithm to obtain the best Eps value and clustering result. Then, we introduce the theory of deviation in the data point spatial distance of the nearest neighbor search mechanism to assign the identified noise points, which solves the problem of over-identification of the algorithm noise points. Finally, we construct color image superpixel information to improve the CSA-DBSCAN algorithm's performance regarding image segmentation. The simulation results of synthetic datasets, real-world datasets, and color images show that the CSA-DBSCAN algorithm can quickly find accurate clustering results and segment color images effectively. The CSA-DBSCAN algorithm has certain clustering effectiveness and practicality.

Fusion-based color and depth image segmentation method for rocks on conveyor belt

Semi-autogenous mill (SAG) feeding process requires accurate and reliable monitoring and controlling techniques for multi-sized rock distribution to stabilize crushing productivity. This study presents a color and depth (RGB-D) image fusion algorithm for the segmentation of multiscale rock images from the conveyor belt. The algorithm combines the multiscale characteristics of the color image and depth image, considers the extraction of large particles from the depth image and the rich texture details for segmentation of the color image, divides the segmentation regions according to different particle sizes, and finally obtains the contour of each particle after region-based superpixels merging. The proposed method outperforms any single sensor image segmentation method proposed in direct comparison, thereby attaining the closest to ground truth performance. The comparison results also show that the accuracy and adaptability of the algorithm are significantly improved, and this algorithm can therefore be used in rock-like material segmentation with multiple size distribution on conveyors.

Investigations of color image segmentation based on connectivity measure, shape priority and normalized fuzzy graph cut

A vital ingredient of image analysis and vision-based systems is color image segmentation studies. Because color images contain more information than gray images, they are more fascinating to segment. Finding the features and associated information of a certain object becomes a difficult operation as a result of the underlying data for color images. Segmentation is highly challenging since color images have a diversity of color image and intensities. In this study, we employ shape priority of color image and connectivity measure as just a thresholding theme to distinguish an object from the surrounding. To introduce a normalized fuzzy graph cut measure based on the common S membership function in order to enhance the segmentation of color images. The execution of the suggested technique, also known as the S-fuzzy normalized graph cut (S-FNGC) approach. The structural imperfections in color images have been handled using the S-fuzzy normalized graph cut. In this algorithm, a system of S fuzzy sets is used to provide information about the specific feature of participation of a particular object in the boundary of the image. In the color image segmentation, the trouble of wrongly segmentation and segmentation with low accuracy can identify with the aid of using this approach. This proposed set of rules is as compared with the mask threshold, Gabor filter, some adaptive techniques like Genetic algorithm (GA), and K-Means clustering algorithm. Moreover, we take a look at that during maximum cases, our set of rules offers the lowest error rate and misclassification error values that enhance the segmentation manner of color images.

Efficient Approach to Color Image Segmentation Based on Multilevel Thresholding Using EMO Algorithm by Considering Spatial Contextual Information.

The process of image segmentation is partitioning an image into its constituent parts and is a significant approach for extracting interesting features from images. Over a couple of decades, many efficient image segmentation approaches have been formulated for various applications. Still, it is a challenging and complex issue, especially for color image segmentation. To moderate this difficulty, a novel multilevel thresholding approach is proposed in this paper based on the electromagnetism optimization (EMO) technique with an energy curve, named multilevel thresholding based on EMO and energy curve (MTEMOE). To compute the optimized threshold values, Otsu's variance and Kapur's entropy are deployed as fitness functions; both values should be maximized to locate optimal threshold values. In both Kapur's and Otsu's methods, the pixels of an image are classified into different classes based on the threshold level selected on the histogram. Optimal threshold levels give higher efficiency of segmentation; the EMO technique is used to find optimal thresholds in this research. The methods based on an image's histograms do not possess the spatial contextual information for finding the optimal threshold levels. To abolish this deficiency an energy curve is used instead of the histogram and this curve can establish the spatial relationship of pixels with their neighbor pixels. To study the experimental results of the proposed scheme, several color benchmark images are considered at various threshold levels and compared with other meta-heuristic algorithms: multi-verse optimization, whale optimization algorithm, and so on. The investigational results are illustrated in terms of mean square error, peak signal-to-noise ratio, the mean value of fitness reach, feature similarity, structural similarity, variation of information, and probability rand index. The results reveal that the proposed MTEMOE approach overtops other state-of-the-art algorithms to solve engineering problems in various fields.

Sign Language Recognition and Response via Virtual Reality

The main aim of this project is to provide a voice to voiceless people with the help of Python and Unity prototype systems. Aforesaid project work focuses on the problem of gesture recognition in real time that sign language is used by the community of deaf people. The problem addressed is based on Digital image processing using color segmentation, Skin detection, Image Segmentation, Image Filtering, and Template Matching techniques. This system recognizes gestures of ISL(Indian Sign Language) including the alphabet and a subset of its words.

Interval type-2 possibilistic fuzzy clustering noisy image segmentation algorithm with adaptive spatial constraints and local feature weighting & clustering weighting

The interval type-2 possibilistic fuzzy C-means (IT2PFCM) algorithm is a popular data clustering and image segmentation method. However, the algorithm fails to consider the spatial information of the image and has limited accuracy for noisy images. The other problem of the IT2PFCM algorithm assumes that all features are equally important in the segmentation process and perform less well as it segments color images. In addition, the IT2PFCM algorithm produces overlapping clusters and even misses some clusters if the groups contained in the data are relatively close to each other. We propose an improved IT2PFCM algorithm for noisy image segmentation to solve the problems. Firstly, an adaptive spatial constraint method is proposed to decrease the algorithm's sensitivity to noise. Secondly, we utilize an automatic local feature weighting and clustering weighting scheme to better segment color images and eliminate overlapping clusters. Finally, an objective function with adaptive spatial constraint, local feature weighting, and clustering weighting is constructed for image segmentation. Extensive experiments on synthetic and natural images show that the proposed algorithm outperforms the state-of-the-art algorithms. The convergence proof of the proposed algorithm is also provided.

An improved mayfly algorithm based on Kapur entropy for multilevel thresholding color image segmentation

Multilevel thresholding segmentation of color images plays an important role in many fields. The pivotal procedure of this technique is determining the specific threshold of the images. In this paper, an improved mayfly algorithm (IMA)-based color image segmentation method is proposed. Tent mapping initializes the female mayfly population to increase population diversity. Lévy flight is introduced in the wedding dance iterative formulation to make IMA jump from the local optimal solution quickly. Two nonlinear coefficients were designed to speed up the convergence of the algorithm. To better verify the effectiveness, eight benchmark functions are used to test the performance of IMA. The average fitness value, standard deviation, and Wilcoxon rank sum test are used as evaluation metrics. The results show that IMA outperforms the comparison algorithm in terms of search accuracy. Furthermore, Kapur entropy is used as the fitness function of IMA to determine the segmentation threshold. 10 Berkeley images are segmented. The best fitness value, peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and other indexes are used to evaluate the effect of segmented images. The results show that the IMA segmentation method improves the segmentation accuracy of color images and obtains higher quality segmented images.

Manta ray foraging optimizer-based image segmentation with a two-strategy enhancement

Image processing is an evolving field that calls for more powerful techniques to extract useful information from images. In particular, image segmentation is a preprocessing step that helps separate objects in a digital image. This article introduces an enhanced manta ray foraging optimizer (MRFO) based on two strategies – oppositional learning (OL) and vertical crossover (VC) search – for color image segmentation. This combination technique focuses on the enhancement of the explorative and exploitative cores, without compromising the computational speed. The proposed algorithm, termed OL-MRFO-VC, is integrated with Kapur entropy to identify the best threshold configuration in each image component (RGB). The technique is tested over three datasets consisting of different scenes. The threshold vector consists of both lower and higher levels in the experiments. In addition, OL-MRFO-VC is compared with fourteen competitive metaheuristics, and eleven measures are used to evaluate their performance quantitatively and qualitatively. According to the computational results, our proposed method outperforms state-of-the-art techniques, especially in the higher threshold levels. Furthermore, the p values in the Wilcoxon signed-rank test confirm a significant improvement brought by our proposed method, suggesting a superior capability of OL-MRFO-VC for solving image segmentation problems.

Colored MRI biomedical image tumor classification and segmentation based on transfer learning of modified Y-Net

By incorporating the colored MRI identification synthesis into the MRI segmentation model with transfer learning AI Y-Net, this study clearly shows the high potential of a multidisciplinary system-level study for diagnoses. This way, such a system can provide integrity of the goal without compromising the quality of each one and saving time consumption. Another alternative to such integration is to be used for enhancement and segmentation that is accurate and robust to the variabilities in scanner and acquisition protocols. System Level Simulator is the deep learning based on Kearse AI deep learning network specified to Y-VGG16 net results of outstanding performance in medical image segmentation. Based on the literature, there are different AI models for the diagnosis system, which are different of what is proposed in this paper. A partial-frozen network is applied to the U-net to compare results between different fine-tuning FT strategies. The network operation is also evaluated depending on the dataset size, showing the importance of the combination of dataset, TL and data augmentation (DA). Transfer learning (TL) helps us for MRI medical image segmentation deep learning with more accurate performances of the TL technique. The system hybrid the Y-Net architecture with Transfer learning to reduce the domain-shift effect in brain MRI segmentation results of the automated deep learning segmentation approach.

The fast multi threshold color image segmentation based on decision rough set

The fast multi threshold color image segmentation based on decision rough set

A Two-Stage Color Image Segmentation Method Based on Saturation-Value Total Variation

A Two-Stage Color Image Segmentation Method Based on Saturation-Value Total Variation

Circular Histogram Breakpoint Selection and Threshold and Color Image Segmentation Method Based on Information Energy

Circular Histogram Breakpoint Selection and Threshold and Color Image Segmentation Method Based on Information Energy

The fast multi-threshold colour image segmentation based on decision rough set

The fast multi-threshold colour image segmentation based on decision rough set