Clustering Algorithm For Image Segmentation Research Articles

A novel spectro-temporal index NDTeI combined with machine learning algorithm for Sentinel-2 cloud detection

ABSTRACT There are challenges for cloud detection over bright surfaces, especially for Sentinel-2 image without thermal band. In this paper, we developed a new cloud detection algorithm for Sentinel-2 data designed to tackle the challenges of cloud detection on bright targets. Our algorithm (called RF-NDTeI-SNIC) was built through the integration of Random Forest (RF) model, Normalized Difference Temporal Index (NDTeI) cloud index and Simple Non-Iterative Clustering (SNIC) image segmentation algorithm. First, RF and NDTeI were used to derive the initial and second stage’s cloud detection results, respectively. Then, the RF- and NDTeI-based results were fused to remove the bright surfaces and land cover changes. Finally, the fusion results were further refined using SNIC for morphological processing. The proposed RF-NDTeI-SNIC algorithm was evaluated using nine challenging cloud-covered Sentinel-2 scenes with substantial bright surfaces. Results indicated that the average overall accuracy of our algorithm was 95.01%, with average commission rate of 10.19% for cloud. In addition, five commonly used Sentinel-2 cloud mask algorithms including s2cloudless, QA, Fmask, CDI and Tmask were selected for comparative analysis. Results suggested that our algorithm outperformed others with overall accuracies of 89.60–94.17% and cloud commission rate of 9.46–37.50%, and have significant advantages in terms of bright surfaces. In summary, the RF-NDTeI-SNIC algorithm we developed was capable of yielding accurate Sentinel-2 cloud masks, and the novel NDTeI cloud index we proposed gave a new and effective approach for improving the separability of clouds and bright surfaces.

Multiview Type-2 kernelized fuzzy C-means clustering with local information for noisy color image segmentation

Focusing on the currently available multi-view fuzzy clustering algorithms, many of which frequently lack robustness and are hence less frequently used in image segmentation. We present a multi-view fuzzy clustering image segmentation algorithm in this research, along with an autonomous view-weight learning mechanism. Firstly, to ensure that each view has the best view weight, the algorithm adds a view weight factor. Secondly, it introduces the weighted fuzzy factor and the kernel distance metric, the role of the weighted fuzzy factor is to collect the local spatial information and local grey scale information to preserve as much of the image’s detailed information as feasible during segmentation. The role of the kernel distance metric is to lessen the influence of outliers and noisy points on image segmentation. Finally, the technique for resolving the issue of image uncertainty and fuzzy factor selection introduces the concept of interval type-2 fuzzy c-means clustering. Numerous experiments on different images demonstrate that the proposed algorithm in this paper is more robust than previous multi-view fuzzy clustering algorithms for solving noise image segmentation problems. It is also more effective at segmenting images contaminated by noise and can better retain the detailed information in the image.

Automatic high depth-resolvability inspection of TBC debonding defects using nonlinear frequency modulation photothermal radar

In this article, a novel nonlinear frequency modulation (NLFM) photothermal radar whose instantaneous frequency curve is a concave quadratic function is proposed and further combines K-means clustering image segmentation algorithm to achieve automatic high depth-resolvability inspection of debonding defects in thermal barrier coating (TBC) structure. Firstly, a 3-dimensional (3D) photothermal finite element (FE) model of TBC structure is established, where the extrapolated Beer-Lambert model is introduced to describe the spatially volumetric heat source within the ceramic coating. Afterwards, the superior depth resolvability of the proposed NLFM photothermal radar is demonstrated compared to conventional waveforms using the established FE model. Meanwhile, its depth resolvability dependence on the thickness and thermal diffusivity of the ceramic coating is also investigated. Finally, the photothermal experiments of the TBC structure containing artificial debonding defects are conducted, and the influence of the coating thickness and metal substrate on the non-destructive testing (NDT) detection of TBC structure by NLFM photothermal radar combining K-means clustering is also experimentally studied. The results reveal that NLFM photothermal radar combining K-means clustering can achieve automatic high depth-resolvability inspection of debonding defects in TBC structure and the coating thickness and metal substrate have also a great influence on the photothermal NDT of the debonding defects of TBC structure.

AENCIC: a method to estimate the number of clusters based on image complexity to be used in fuzzy clustering algorithms for image segmentation

AENCIC: a method to estimate the number of clusters based on image complexity to be used in fuzzy clustering algorithms for image segmentation

A knee point driven Kriging-assisted multi-objective robust fuzzy clustering algorithm for image segmentation

To enhance the segmentation performance and optimization efficiency of multi-objective evolutionary clustering algorithms for noisy images, this study presents a knee point driven Kriging-assisted multi-objective robust fuzzy clustering algorithm for image segmentation (K2 MORFC). First, pixel and region-level fitness functions are developed to comprehensively consider the spatial constraints and region consistency in an image. In the pixel-level fitness function, a Kullback–Leibler (KL) divergence-based spatial constraint term is employed and combined with intra-class compactness to preserve the membership similarity between an arbitrary image pixel and its non-local neighbors. In the region-level fitness function, image edge information is utilized to evaluate the regional consistency of the partition. Moreover, an adaptive determination mechanism of the weight factor for the spatial constraint term is designed in the pixel-level fitness function to control the influence of the spatial constraint for each pixel. Furthermore, considering that the preference toward knee points can effectively improve the convergence performance, a knee point driven Kriging-assisted evolutionary algorithm (K2EA) is proposed to efficiently optimize these two fitness functions, including a dynamic subspace knee point searching strategy, a knee point guided environmental selection strategy, and a Kriging model management mechanism. Finally, we construct a fuzzy clustering validity index with a KL divergence-based non-local spatial constraint term to select the optimal solution from the nondominated solution set. Competitive experimental results on the DTLZ benchmark verify the effectiveness of K2EA. Segmentation results on color images from the Berkeley dataset and magnetic resonance images from the BrainWeb and IBSR datasets confirm the performance of K2MORFC.

Convolution Optimized Density Clustering Algorithm for Image Segmentation

In order to improve the usability of DBSCAN in the field of high-resolution image segmentation, according to the characteristics of digital images, an efficient DBSCAN based on three-dimensional convolution is proposed, and on this basis, a color image segmentation method is implemented. First, convolution is introduced into DBSCAN, three-dimensional convolution is used to obtain the core object, avoiding distance calculation and region retrieval, and reducing the time complexity of this step from the second order to a constant level. Then, the three-dimensional convolution is optimized based on dynamic programming, so that the convolution time is independent of the size of the convolution kernel. Finally, the neighborhood search range of the sample points is simplified according to the ordered core object set. Compared with the existing algorithms, the experimental results on the Berkeley BSD300 show that the algorithm is effective, the CH index and the time efficiency increase by 17% and 49% on average. The results of experiments using the remote sensing image set Mts-WH show that the computational efficiency of the algorithm is significantly improved. Compared with the DBSCAN optimized by the <italic>k</italic>D tree, the efficiency is increased by an average of 19 times, and the improvement effect is more obvious as the number of sample points increases. When the number of points exceeds 500 000, the efficiency is increased by 169 times.

Fuzzy C-means Clustering Image Segmentation Algorithm Based on Hidden Markov Model

Fuzzy C-means Clustering Image Segmentation Algorithm Based on Hidden Markov Model

Surface Defect Detection of Seals Based on K-Means Clustering Algorithm and Particle Swarm Optimization

As an important part of automobile, the quality and safety of automobile engine high-pressure oil circuit seal parts are an important indicator of the manufacturer’s production process. In order to improve the detection accuracy and efficiency of seal parts in the traditional production process, the defect detection method on the surface of the seal was studied. A K-Means clustering image segmentation algorithm based on particle swarm optimization was proposed. To detect the surface defects of seals, first, preprocess the seal image. Then, use the SURF algorithm to extract the feature points of the seal image. Finally, according to the particle swarm fitness variance function, select the insertion point calculated by combining particle swarm optimization and K-Means algorithm. Through iteration, optimize the initial clustering center of K-Means algorithm. The efficiency of K-Means algorithm clustering iteration is improved. The test verifies the applicability of the algorithm in the actual process, and it can be used to accurately detect seals. Experimental results show that the detection accuracy rate reaches 98%, which is highly applicable to the actual production.

Peak-Graph-Based Fast Density Peak Clustering for Image Segmentation

Fuzzy c-means (FCM) algorithm as a traditional clustering algorithm for image segmentation cannot effectively preserve local spatial information of pixels, which leads to poor segmentation results with inconsistent regions. For the remedy, superpixel technologies are applied, but spatial information preservation highly relies on the quality of superpixels. Density peak clustering algorithm (DPC) can reconstruct spatial information of arbitrary-shaped clusters, but its high time complexity $O(n^2)$ and unrobust allocation strategy decrease its applicability for image segmentation. Herein, a fast density peak clustering method (PGDPC) based on the kNN distance matrix of data with time complexity $O(nlog(n))$ is proposed. By using the peak-graph-based allocation strategy, PGDPC is more robust in the reconstruction of spatial information of various complex-shaped clusters, so it can rapidly and accurately segment images into high-consistent segmentation regions. Experiments on synthetic datasets, real and Wireless Capsule Endoscopy (WCE) images demonstrate that PGDPC as a fast and robust clustering algorithm is applicable to image segmentation.

A Fast K-medoids Clustering Algorithm for Image Segmentation based Object Recognition

The segmentation of color images as a preprocessing to recognize objects is an important computer vision technique for robotic environment modeling. Linking image sequences to identify all the segments belonging to the same objects is a crucial and challenging problem, especially given the large volumes of image data. In this paper, we propose an aggregation-based fast K-medoids clustering algorithm as a solution for an efficient as well as reliable image segmentation and object recognition problem.

Robust entropy-based symmetric regularized picture fuzzy clustering for image segmentation

Symmetric regularized picture fuzzy clustering is a new fuzzy clustering method, and it is difficult to choose its weighting fuzzy factor m and lacks certain robustness to noises or outliers. To this end, this paper proposes a robust entropy-based symmetric regularized picture fuzzy clustering with spatial information constraints for noisy image segmentation. The idea of maximum entropy fuzzy clustering is firstly introduced into symmetric regularized picture fuzzy clustering, and we can obtain an entropy-based symmetric regularized picture fuzzy clustering method to avoid selecting weighting fuzzy factors m. Meanwhile, it has clear physical meaning. Then, spatial neighborhood information of current clustering pixel is embedded into its clustering objective function, and a new robust fuzzy clustering algorithm for image segmentation is obtained to enhance the ability to suppress noise. In the end, the convergence of new robust clustering algorithm is proved by Zangwill theorem. Many experimental results show that the proposed algorithm can achieve higher segmentation performance than existing picture fuzzy clustering.

Improved clustering algorithms for image segmentation based on non-local information and back projection

Accurate image segmentation is a prerequisite to conducting an image analysis task, and the complexity stemming from the semantic diversity plays a pivotal role in image segmentation. Existing algorithms employed different types of information in the process of segmentation to improve the robustness. However, these algorithms were characterized by a tradeoff between noise removal and detail retention; this is because it is difficult to distinguish image artifacts from details. This paper proposes an improved image segmentation schema and presents two improved clustering algorithms, in which self-similarity and back projection are considered simultaneously to enhance the robustness. With the aid of self-similarity, non-local information is fully exploited, while the original information can be retained by back projection. Extensive experiments on various types of images demonstrate that our algorithms can balance noise restraining and detail retention to improve the adaptation of complex images in segmentation.

Automatic clustering using a local search-based human mental search algorithm for image segmentation

Clustering is a commonly employed approach to image segmentation. To overcome the problems of conventional algorithms such as getting trapped in local optima, in this paper, we propose an improved automatic clustering algorithm for image segmentation based on the human mental search (HMS) algorithm, a recently proposed method to solve complex optimisation problems. In contrast to most existing methods for image clustering, our approach does not require any prior knowledge about the number of clusters but rather determines the optimal number of clusters automatically. In addition, for further improved efficacy, we incorporate local search operators which are designed to make changes to the current cluster configuration.To evaluate the performance of our proposed algorithm, we perform an extensive comparison with several state-of-the-art algorithms on a benchmark set of images and using a variety of metrics including cost function, correctness of the obtained numbers of clusters, stability, as well as supervised and unsupervised segmentation criteria. The obtained results clearly indicate excellent performance compared to existing methods with our approach yielding the best result in 16 of 17 cases based on cost function evaluation, 9 of 11 cases based on number of identified clusters, 13 of 17 cases based on the unsupervised Borsotti image segmentation criterion, and 7 of 11 cases based on the supervised PRI image segmentation metric.

An Adaptive Feature Selection Algorithm for Fuzzy Clustering Image Segmentation Based on Embedded Neighbourhood Information Constraints.

This paper addresses the lack of robustness of feature selection algorithms for fuzzy clustering segmentation with the Gaussian mixture model. Assuming that the neighbourhood pixels and the centre pixels obey the same distribution, a Markov method is introduced to construct the prior probability distribution and achieve the membership degree regularisation constraint for clustering sample points. Then, a noise smoothing factor is introduced to optimise the prior probability constraint. Second, a power index is constructed by combining the classification membership degree and prior probability since the Kullback–Leibler (KL) divergence of the noise smoothing factor is used to supervise the prior probability; this probability is embedded into Fuzzy Superpixels Fuzzy C-means (FSFCM) as a regular factor. This paper proposes a fuzzy clustering image segmentation algorithm based on an adaptive feature selection Gaussian mixture model with neighbourhood information constraints. To verify the segmentation performance and anti-noise robustness of the improved algorithm, the fuzzy C-means clustering algorithm Fuzzy C-means (FCM), FSFCM, Spatially Variant Finite Mixture Model (SVFMM), EGFMM, extended Gaussian mixture model (EGMM), adaptive feature selection robust fuzzy clustering segmentation algorithm (AFSFCM), fast and robust spatially constrained Gaussian mixture model (GMM) for image segmentation (FRSCGMM), and improve method are used to segment grey images containing Gaussian noise, salt-and-pepper noise, multiplicative noise and mixed noise. The peak signal-to-noise ratio (PSNR) and the error rate (MCR) are used as the theoretical basis for assessing the segmentation results. The improved algorithm indicators proposed in this paper are optimised. The improved algorithm yields increases of 0.1272–12.9803 dB, 1.5501–13.4396 dB, 1.9113–11.2613 dB and 1.0233–10.2804 dB over the other methods, and the Misclassification rate (MSR) decreases by 0.32–37.32%, 5.02–41.05%, 0.3–21.79% and 0.9–30.95% compared to that with the other algorithms. It is verified that the segmentation results of the improved algorithm have good regional consistency and strong anti-noise robustness, and they meet the needs of noisy image segmentation.

Intuitionistic Fuzzy Clustering Image Segmentation Based on Flower Pollination Optimization with Nearest Neighbor Searching

In order to overcome shortcomings of the traditional fuzzy clustering algorithm for image segmentation, such as that are easily affected by noise, sensitive to the initial value of clustering center, easily falling into local optimum, and inadequate ability of fuzzy information processing, an intuitionistic fuzzy clustering image segmentation algorithm is proposed based on flower pollination optimization with nearest neighbor searching. Firstly, a novel extraction strategy of image spatial information is proposed, and then an intuitionistic fuzzy clustering objective function with image spatial information is constructed to improve the algorithm’s robustness against noise and enhance the ability of the algorithm to process the image fuzzy information. In order to overcome the defects of sensitivity to clustering centers and easily falling into local optimum, a flower pollination algorithm based on nearest neighbor learning search mechanism is proposed. Experimental results show that the proposed method can get satisfactory segmentation results on a variety of noisy images.

FCM fuzzy clustering image segmentation algorithm based on fractional particle swarm optimization

FCM fuzzy clustering image segmentation algorithm based on fractional particle swarm optimization

A Local Neighborhood Robust Fuzzy Clustering Image Segmentation Algorithm Based on an Adaptive Feature Selection Gaussian Mixture Model.

Since the fuzzy local information C-means (FLICM) segmentation algorithm cannot take into account the impact of different features on clustering segmentation results, a local fuzzy clustering segmentation algorithm based on a feature selection Gaussian mixture model was proposed. First, the constraints of the membership degree on the spatial distance were added to the local information function. Second, the feature saliency was introduced into the objective function. By using the Lagrange multiplier method, the optimal expression of the objective function was solved. Neighborhood weighting information was added to the iteration expression of the classification membership degree to obtain a local feature selection based on feature selection. Each of the improved FLICM algorithm, the fuzzy C-means with spatial constraints (FCM_S) algorithm, and the original FLICM algorithm were then used to cluster and segment the interference images of Gaussian noise, salt-and-pepper noise, multiplicative noise, and mixed noise. The performances of the peak signal-to-noise ratio and error rate of the segmentation results were compared with each other. At the same time, the iteration time and number of iterations used to converge the objective function of the algorithm were compared. In summary, the improved algorithm significantly improved the ability of image noise suppression under strong noise interference, improved the efficiency of operation, facilitated remote sensing image capture under strong noise interference, and promoted the development of a robust anti-noise fuzzy clustering algorithm.

Manifold Spectral Clustering Image Segmentation Algorithm Based on Local Geometry Features

To improve the accuracy and timeliness of spectral clustering image segmentation,an algorithm of manifold spectral clustering image segmentation based on local geometry features is proposed.Firstly,considering the manifold structure of image data,the relationship of data intrinsic dimensions is obtained by performing spectral clustering based on local principal components analysis in the k-nearest neighbor region of data points.Then,the local linear reconstruction technique in manifold learning is introduced,and the similarity of local tangent space between data is obtained via mixed linear analyzers,and the similarity matrix with local geometric features is constructed by merging the intrinsic dimension and the local tangent space.Nystr m technique is utilized to approximate eigenvectors of the image to be segmented,and spectral clustering is performed on the constructed k principal eigenvectors.Finally,experiments on Berkeley dataset show the advantages of the proposed algorithm in accuracy and timeliness.

A multimodal particle swarm optimization-based approach for image segmentation

Color image segmentation is a fundamental challenge in the field of image analysis and pattern recognition. In this paper, a novel automated pixel clustering and color image segmentation algorithm is presented. The proposed method operates in three successive stages. In the first stage, a three-dimensional histogram of pixel colors based on the RGB model is smoothened using a Gaussian filter. This process helps to eliminate unreliable and non-dominating peaks that are too close to one another in the histogram. In the next stage, the peaks representing different clusters in the histogram are identified using a multimodal particle swarm optimization algorithm. Finally, pixels are assigned to the most appropriate cluster based on Euclidean distance. Determining the number of clusters to be used is often a manual process left for a user and represents a challenge for various segmentation algorithms. The proposed method is designed to determine an appropriate number of clusters, in addition to the actual peaks, automatically. Experiments confirm that the proposed approach yields desirable results, demonstrating that it can find an appropriate set of clusters for a set of well-known benchmark images.

Experimental evaluation of nonparametric clustering algorithms for image segmentation

Experimental evaluation of 12 nonparametric clustering algorithms for image segmentation was made. Algorithms developed in FRC ICT are compared to ones from ENVI, ELKI and Smile software packages. Seven model datasets were generated to estimate clustering accuracy. The computational efficiency was evaluated using digital photographs and fragments of multispectral images obtained from WorldView-2 satellite.