A Spatial Clustering Method With Edge Weighting for Image Segmentation

spatial clustering method can be used for the analysis of satellite image to get the meaningful information with the help of fuzzy local information c-means algorithm using image segmentation technique. In previous clustering based image segmentation method may lose the important image details due to the noise present in the satellite image. The effects of noise are avoided by the spatial relationship among pixels, but it often generates boundary zones for the mix of pixel around the edges .To overcome these problem (FELICM) reduces the edge degradation by introducing the weights of pixel within the local neighboring windows. Canny edge detection used for the extraction of edges, during that detection multi-Otsu threshold can be used to obtain two adaptive thresholds. Local neighbors and window center are separated by edge with respect to different weights are set to the windows. Until the final clustering result may be obtained in a efficient manner the pixel of the different local neighbor window periodically iterated. Without any filter preprocessing steps FELICM method can directly applied to the satellite image to get the valuable information when compared to the remote sensing image of a experimental results and not only solves the problem of isolation of samples and random distribution of pixels inside the region but it also produce the high edge accuracies.

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] The possibilistic c-means (PCM) was developed as an extension of the fuzzy c-means (FCM) clustering algorithm by abandoning the membership sum-to-one constraint. In the PCM, each cluster is independent of the other clusters and can be processed separately. Because of this separability, the sequential possibilistic one-mean (SP1M) was proposed to find clusters sequentially by running the possibilistic one-mean (P1M) c times. One critical problem in both PCM and SP1M is how to determine the parameter [subscript]. The sequential possibilistic one-mean with adaptive eta (SP1M-AE) was developed to allow [subscript] to change during iterations. In this thesis, a new dynamic adaptation mechanism for the parameter [subscript] in each cluster is inserted into SP1M. The resultant algorithm, called the sequential possibilistic one-mean with dynamic eta (SP1M-DE) is shown to provide superior performance over PCM, SP1M, and SP1M-AE in determining correct clustering results. In this thesis, the family of the SP1M clustering algorithm is also extended to combine local spatial information in an image. The resultant algorithm, called sequential possibilistic local information one-mean (SPLI1M) is shown to have a better performance on image segmentation over the fuzzy local information c-means (FLICM), the possibilistic local information c-means (PLICM), and the family of SP1M without combing local spatial information in image.

Lessons to learn from a mistaken optimization

In this work, a new image segmentation algorithm is introduced. The proposed algorithm combines the results of an hybrid clustering ensemble. The ensemble clustering is composed of fuzzy c-means (FCM) algorithm and fuzzy local information c-means (FCM_S1 and FCM_S2) algorithms with different values of the neighbors effect. The consensus technique is performed by the ordered weighted averaging (OWA) method. The weight attributed to each classifier can be modified during the process of classification and is determined by the classifier results of the pixel neighbors classification. Dynamic weights give the method the ability to select the temporal best performance during the classification process. Experiments performed on a synthetic image, and real images show that the proposed algorithm is effective and efficient and provides good noise elimination effect.

The classic Fuzzy C-means (FCM) algorithm has limited clustering performance and is prone to misclassification of border points. This study offers a bi-directional FCM clustering ensemble approach that takes local information into account (LI_BIFCM) to overcome these challenges and increase clustering quality. First, various membership matrices are created after running FCM multiple times, based on the randomization of the initial cluster centers, and a vertical ensemble is performed using the maximum membership principle. Second, after each execution of FCM, multiple local membership matrices of the sample points are created using multiple K-nearest neighbors, and a horizontal ensemble is performed. Multiple horizontal ensembles can be created using multiple FCM clustering. Finally, the final clustering results are obtained by combining the vertical and horizontal clustering ensembles. Twelve data sets were chosen for testing from both synthetic and real data sources. The LI_BIFCM clustering performance outperformed four traditional clustering algorithms and three clustering ensemble algorithms in the experiments. Furthermore, the final clustering results has a weak correlation with the bi-directional cluster ensemble parameters, indicating that the suggested technique is robust.

Fuzzy C-Means (FCM) algorithm is the most popular method used in image segmentation for clustering because it has robust characteristics for ambiguity and can retain much more information than hard segmentation methods. Although the conventional FCM algorithm works well on most noise-free images, it is very sensitive to noise and other imaging artifacts, since it does not consider any information about spatial context. We propose a new method for image clustering by variation of traditional fuzzy c-means algorithm to provide noise free extracted image. In the new method local spatial information and gray level information is incorporated into it. The new method is called FLICM and it is fully free of empirically adjusted parameters and provides robustness to noisy images. RGB image is converted to gray intensity image by HSI model. The gray image is clustered using FLICM algorithm and it is converted again to segmented color image. Segmentation in intensity band considerably reduces the time of processing instead of processing the three bands of the RGB model and it results in high degree of accuracy.

To implement efficient computer vision mechanisms, efficient image clustering methods are important. The paper elaborates a clustering method based on particle swarm optimization (PSO) which provides automatic establishment of clustering parameters. The developed PSO based clustering method was tested on 860 images for a car vision system and its results and contribution to the pattern recognition quality improvement were assessed in comparison with fuzzy C-means and k-means. The results do not differ significantly, but distinction in average time of work for these methods was noted. The PSO clustering method is faster than k-means and slower than fuzzy C-means. However, fuzzy C-means method does not guarantee correct results during the further analysis, so the PSO clustering method can be more efficient for implementation in computer vision systems.

A novel kernelized total Bregman divergence-based fuzzy clustering with local information for image segmentation

Spatial clustering has been widely used for spatial data mining and knowledge discovery. An ideal multivariate spatial clustering should consider both spatial contiguity and aspatial attributes. Existing spatial clustering approaches may face challenges for discovering repeated geographic patterns with spatial contiguity maintained. In this paper, we propose a Spatial Toeplitz Inverse Covariance-Based Clustering (STICC) method that considers both attributes and spatial relationships of geographic objects for multivariate spatial clustering. A subregion is created for each geographic object serving as the basic unit when performing clustering. A Markov random field is then constructed to characterize the attribute dependencies of subregions. Using a spatial consistency strategy, nearby objects are encouraged to belong to the same cluster. To test the performance of the proposed STICC algorithm, we apply it in two use cases. The comparison results with several baseline methods show that the STICC outperforms others significantly in terms of adjusted rand index and macro-F1 score. Join count statistics is also calculated and shows that the spatial contiguity is well preserved by STICC. Such a spatial clustering method may benefit various applications in the fields of geography, remote sensing, transportation, and urban planning, etc.

Segmentation of white matter, grey matter and cerebrospinal fluid from brain MR images using a modified FCM based on double estimation

目的 随着实际应用场景中海量数据采集技术的发展和数据标注成本的不断增加，自监督学习成为海量数据分析的一个重要策略。然而，如何从海量数据中抽取有用的监督信息，并该监督信息下开展有效的学习仍然是制约该方向发展的研究难点。为此，提出了一个基于共识图学习的自监督集成聚类框架。方法 框架主要包括3个功能模块。首先，利用集成学习中多个基学习器构建共识图；其次，利用图神经网络分析共识图，捕获节点优化表示和节点的聚类结构，并从聚类中挑选高置信度的节点子集及对应的类标签生成监督信息；再次，在此标签监督下，联合其他无标注样本更新集成成员基学习器。交替迭代上述功能块，最终提高无监督聚类的性能。结果 为验证该框架的有效性，在标准数据集（包括图像和文本数据）上设计了一系列实验。实验结果表明，所提方法在性能上一致优于现有聚类方法。尤其是在MNIST-Test （modified national institute of standards and technology database）上，本文方法实现了97.78%的准确率，比已有最佳方法高出3.85%。结论 该方法旨在利用图表示学习提升自监督学习中监督信息捕获的能力，监督信息的有效获取进一步强化了集成学习中成员构建的能力，最终提升了无监督海量数据本质结构的挖掘性能。;Objective Clustering is focused on machine learning-related data segmentation for multiple datasets. Its applications are in relevant to such domains like image segmentation and anomaly detection. In addition，to simplify complex tasks optimize its performance，clustering is used in data preprocessing tasks of those are data sub-blocks segmentation， pseudo-labels generation，and abnormal points-removal. Self-supervised learning has become an essential technique for massive data analysis. However，it is challenged to extract effective supervision information and analyze the input data. Method A consensus graph learning based self-supervised ensemble clustering（CGL-SEC）framework is developed. It consists of three main modules：1）to construct the consensus graph based on several ensemble components（i. e. ，the basic clustering methods）. 2）to extract the supervision information by learning the consensus graph representation，and 3）its node clustering results，where the subset of nodes with the high-confidence are selected as labeled samples. To optimize the ensemble components and the corresponding consensus graph，t basic clustering methods are re-trained in related the option of samples-labeled and other samples-unlabeled. The final clustering results can be optimized iteratively until the learning process converges. Result A series of experiments are carried out on benchmarks，including both image and textual datasets. Especially，CGL-SEC is 3. 85% over baseline in terms of clustering evaluation metric on themodified national institute of standards and technology database（MNIST-Test）. First，to optimize data representation and cluster assignment at the same time，deep embedding clustering can be focused on data itself as the supervision information and auto-encoder with the reconstruction loss is pre-trained. The soft cluster assignment of features-embedded is then calculated，and the KL（Kullback-Leibler）divergence is minimized between the soft cluster assignment and the auxiliary target distribution. To improve the performance of the model further，following deep clustering network（DCN）can use hard clustering instead of soft allocation，and local constraints are applied by improved deep embedding clustering（IDEC）. The pseudo-label strategy is implemented as a self-supervised learning method that uses the prediction results of the neural network as the label to simulate the supervision information compared to using data itself as the supervision information. Deepcluster-based K-means clustering is used to generate pseudo-labels to guide the training of convolutional networks. However，the generated pseudo-labels have lower confidence and are prone to trivial solutions in the initial stage of network training. Deep embedding clustering with data augmentation（DEC-DA）and MixMatch-based prediction of data-enhanced samples are used as the supervision information of the original data，which improves the accuracy of the supervision information to a certain extent，but this method is difficult to extend to text and other fields. Deep adaptive clustering-based high-confidence pseudo-label subsets-selected are iteratively trained the network in the prediction results，but lowconfidence samples-involved data distribution information is ignored. Pseudo-semi-supervised clustering votes are used to select a subset of high-confidence pseudo-labels，and all samples are used to train semi-supervised neural network. Although the ensemble strategy can improve the confidence of the pseudo-label，the voting strategy is concerned of category representation only without the feature representation of the sample itself，which can reduce the clustering performance in some cases. The ensemble learning is regarded as a representative machine learning method that reflects the ability of group intelligence，whereas a learning method can improve the overall prediction performance via multiple base learners training and their coordinated prediction results. In pseudo-label-based clustering tasks，it can coordinate multiple base learners to obtain high-confidence pseudo-labels. However，the effectiveness of the supervision information acquisition is still to be resolved. The category information of the sample is considered for current pseudo-label-based ensemble clustering method only when the label is captured and some effective information are ignored like the feature representation of the sample itself and the clustering structure between samples. Conclusion Graph neural network is composed of content information of nodes and the structural information between nodes at the same time. To design a self-supervised ensemble clustering method based on consensus graph representation learning，it is required to make full use of sample features and relationships between samples in ensemble learning. To obtain higher confidence pseudo-labels as supervised information and improve the performance of self-supervised clustering，it is necessary to mine global and local information at the same time. We illustrate a learnable data ensemble representation through graph neural network. The confidence of pseudo-labels is improved，and the entire model is trained in self-supervision iteratively. To be summarized：1）Commonly-used consensus graph learning-integrated clustering framework is developed，which can use multi-level information like clusteringintegrated sample characteristics and category structure. 2）Self-supervision method is proposed，which uses graph neural network to mine the global and local information of the consensus graph，and high-confidence pseudo-labels are obtained as supervised information. 3）Experiments are demonstrated that the consensus graph learning ensemble clustering method has its potentials on image and text datasets.

Nonlinear subspace clustering for image clustering

This article proposes a new recoloring method for textile fabric images based on improved fuzzy local information c‐means (FLICM) clustering. In the clustering algorithm, the fuzzy factor was modified so that it can produce reliable segmentation in areas with rich details. With the obtained cluster labels and pixel‐wise memberships, the color of each pixel is modeled as the linear combination of the two most dominant colors. The recoloring process was then conducted by replacing the specified dominant color with user‐provided target colors. Experimental results showed that the proposed method can produce natural and faithful color appearance on both printed and yarn‐dyed fabric images, and outperforms the state‐of‐the‐art. © 2016 Wiley Periodicals, Inc. Col Res Appl, 42, 115–123, 2017

<span id="docs-internal-guid-94842888-7fff-2ae1-cd5c-026943b95b7f"><span>Cancer is one of the diseases with the highest mortality rate in the world. Cancer is a disease when abnormal cells grow out of control that can attack the body's organs side by side or spread to other organs. Lung cancer is a condition when malignant cells form in the lungs. To diagnose lung cancer can be done by taking x-ray images, CT scans, and lung tissue biopsy. In this modern era, technology is expected to help research in the field of health. Therefore, in this study feature extraction from CT images was used as data to classify lung cancer. We used CT scan image data from SPIE-AAPM Lung CT challenge 2015. Fuzzy C-Means and fuzzy kernel C-Means were used to classify the lung nodule from the patient into benign or malignant. Fuzzy C-Means is a soft clustering method that uses Euclidean distance to calculate the cluster center and membership matrix. Whereas fuzzy kernel C-Means uses kernel distance to calculate it. In addition, the support vector machine was used in another study to obtain 72% average AUC. Simulations were performed using different k-folds. The score showed fuzzy kernel C-Means had the highest accuracy of 74%, while fuzzy C-Means obtained 73% accuracy. </span></span>

Knowing and defining the spatial and temporal variability of soil chemical properties becomes important for soil management. The definition of application zones in agricultural areas consists of dividing the area into homogeneous subareas, thus allowing the development of localized management. These zones can be defined by cluster methods and one of their advantages is to direct the determination of a future soil sampling, with a possible sample reduction. This study aimed to propose a methodology that integrates multivariate and space-time analyses to obtain an optimized sample configuration, using spatio-temporal data and application zones. First, three spatial multivariate clustering methods were evaluated to obtain the application zone (fuzzy C-means, K-means and Ward). Clusters were obtained by considering the analysis of spatio-temporal dependence through the empirical orthogonal function. Afterward, 50, 60 and 70 % of the sampling points of the initial sampling configuration were selected in each application zone, generating an optimized sample configuration. This choice was made by an optimization process, whose efficiency was evaluated based on spatial prediction, using the sum of the Overall Accuracy Index, of all soil properties. The results indicated the division of the agricultural area into two application zones, considering the K-means method. For most soil properties, when comparing the original and reduced sampling configurations, a similarity was observed in descriptive statistics. Regarding the estimates of the accuracy indexes, considering all the optimized sample configurations, the best estimates were observed comparing the initial sample configurations and the optimized ones in 70 % for most soil chemical properties.