Fuzzy Similarity Measure Based Spectral Clustering Framework for Noisy Image Segmentation

2017 ◽  
Vol 25 (04) ◽  
pp. 649-673 ◽  
Author(s):  
Subhanshu Goyal ◽  
Sushil Kumar ◽  
M. A. Zaveri ◽  
A. K. Shukla
Keyword(s):  
Similarity Measure ◽  
Spectral Clustering ◽  
Clustering Algorithm ◽  
Spatial Information ◽  
Clustering Algorithms ◽  
Gaussian Kernel ◽  
Similarity Matrix ◽  
Fuzzy Similarity ◽  
Level Information ◽  
Fuzzy C Means Clustering

In recent times, graph based spectral clustering algorithms have received immense attention in many areas like, data mining, object recognition, image analysis and processing. The commonly used similarity measure in the clustering algorithms is the Gaussian kernel function which uses sensitive scaling parameter and when applied to the segmentation of noise contaminated images leads to unsatisfactory performance because of neglecting the spatial pixel information. The present work introduces a novel framework for spectral clustering which embodied local spatial information and fuzzy based similarity measure to tackle the above mentioned issues. In our approach, firstly we filter the noise components from original image by using the spatial and gray–level information. The similarity matrix is then constructed by employing a similarity measure which takes into account the fuzzy c-partition matrix and vectors of the cluster centers obtained by fuzzy c-means clustering algorithm. In the last step, spectral clustering technique is realized on derived similarity matrix to obtain the desired segmentation result. Experimental results on segmentation of synthetic and Berkeley benchmark images with noise demonstrates the effectiveness and robustness of the proposed method, giving it an edge over the clustering based segmentation method reported in the literature.

scholarly journals Brain Tissue Segmentation and Bias Field Correction of MR Image Based on Spatially Coherent FCM with Nonlocal Constraints

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Jianhua Song ◽  
Zhe Zhang
Keyword(s):  
Magnetic Resonance ◽  
Objective Function ◽  
Similarity Measure ◽  
Clustering Algorithm ◽  
Spatial Information ◽  
Bias Field ◽  
Gaussian Kernel ◽  
Intensity Inhomogeneity ◽  
Tissue Segmentation ◽  
Mr Image

Influenced by poor radio frequency field uniformity and gradient-driven eddy currents, intensity inhomogeneity (or bias field) and noise appear in brain magnetic resonance (MR) image. However, some traditional fuzzy c-means clustering algorithms with local spatial constraints often cannot obtain satisfactory segmentation performance. Therefore, an objective function based on spatial coherence for brain MR image segmentation and intensity inhomogeneity correction simultaneously is constructed in this paper. First, a novel similarity measure including local neighboring information is designed to improve the separability of MR data in Gaussian kernel mapping space without image smoothing, and the similarity measure incorporates the spatial distance and grayscale difference between cluster centroid and its neighborhood pixels. Second, the objective function with an adaptive nonlocal spatial regularization term is drawn upon to compensate the drawback of the local spatial information. Meanwhile, bias field information is also embedded into the similarity measure of clustering algorithm. From the comparison between the proposed algorithm and the state-of-the-art methods, our model is more robust to noise in the brain magnetic resonance image, and the bias field is also effectively estimated.

scholarly journals Spectral Clustering Algorithm Based on Improved Gaussian Kernel Function and Beetle Antennae Search with Damping Factor

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Zhe Zhang ◽  
Xiyu Liu ◽  
Lin Wang
Keyword(s):  
Kernel Function ◽  
Spectral Clustering ◽  
Clustering Algorithm ◽  
Gaussian Kernel ◽  
Damping Factor ◽  
Data Sets ◽  
Similarity Matrix ◽  
Scale Parameters ◽  
Gaussian Kernel Function ◽  
Spectral Clustering Algorithm

There are two problems in the traditional spectral clustering algorithm. Firstly, when it uses Gaussian kernel function to construct the similarity matrix, different scale parameters in Gaussian kernel function will lead to different results of the algorithm. Secondly, K-means algorithm is often used in the clustering stage of the spectral clustering algorithm. It needs to initialize the cluster center randomly, which will result in the instability of the results. In this paper, an improved spectral clustering algorithm is proposed to solve these two problems. In constructing a similarity matrix, we proposed an improved Gaussian kernel function, which is based on the distance information of some nearest neighbors and can adaptively select scale parameters. In the clustering stage, beetle antennae search algorithm with damping factor is proposed to complete the clustering to overcome the problem of instability of the clustering results. In the experiment, we use four artificial data sets and seven UCI data sets to verify the performance of our algorithm. In addition, four images in BSDS500 image data sets are segmented in this paper, and the results show that our algorithm is better than other comparison algorithms in image segmentation.

Spectral Clustering Algorithm: MATLAB PCT-Based Parallel Design and Implementation

2013 ◽  
Vol 765-767 ◽  
pp. 580-584
Author(s):  
Yu Yang ◽  
Cheng Gui Zhao
Keyword(s):  
Spectral Clustering ◽  
Large Scale ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Parallel Structure ◽  
Computational Time ◽  
Similarity Matrix ◽  
Data Intensive ◽  
Clustering Quality ◽  
Spectral Clustering Algorithm

Spectral clustering algorithms inevitable exist computational time and memory use problems for large-scale spectral clustering, owing to compute-intensive and data-intensive. We analyse the time complexity of constructing similarity matrix, doing eigendecomposition and performing k-means and exploiting SPMD parallel structure supported by MATLAB Parallel Computing Toolbox (PCT) to decrease eigendecomposition computational time. We propose using MATLAB Distributed Computing Server to parallel construct similarity matrix, whilst using t-nearest neighbors approach to reduce memory use. Ultimately, we present clustering time, clustering quality and clustering accuracy in the experiments.

An Empirical Analysis of Similarity Matrix for Spectral Clustering

2013 ◽  
Vol 433-435 ◽  
pp. 725-730
Author(s):  
Sheng Zhang ◽  
Xiao Qi He ◽  
Yang Guang Liu ◽  
Qi Chun Huang
Keyword(s):  
Spectral Clustering ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Similarity Matrix ◽  
Real Word ◽  
Normalized Mutual Information ◽  
Analysis Of Similarity ◽  
Data Points ◽  
Synthetic Datasets ◽  
Multiple Metrics

Constructing the similarity matrix is the key step for spectral clustering, and its goal is to model the local neighborhood relationships between the data points. In order to evaluate the influence of similarity matrix on performance of the different spectral clustering algorithms and find the rules on how to construct an appropriate similarity matrix, a system empirical study was carried out. In the study, six recently proposed spectral clustering algorithms were selected as evaluation object, and normalized mutual information, F-measures and Rand Index were used as evaluation metrics. Then experiments were carried out on eight synthetic datasets and eleven real word datasets respectively. The experimental results show that with multiple metrics the results are more comprehensive and confident, and the comprehensive performance of locality spectral clustering algorithm is better than other five algorithms on synthetic datasets and real word datasets.

Handling WSD using Hierarchical Clustering Algorithm with sentences

2018 ◽  
pp. 83-88
Author(s):  
Mohana Priya K ◽  
Pooja Ragavi S ◽  
Krishna Priya G
Keyword(s):  
Hierarchical Clustering ◽  
Similarity Measure ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Cosine Similarity Measure ◽  
Hierarchical Clustering Algorithm ◽  
Multiple Levels ◽  
Pos Tagger ◽  
Sentence Clustering ◽  
The Right

Clustering is the process of grouping objects into subsets that have meaning in the context of a particular problem. It does not rely on predefined classes. It is referred to as an unsupervised learning method because no information is provided about the "right answer" for any of the objects. Many clustering algorithms have been proposed and are used based on different applications. Sentence clustering is one of best clustering technique. Hierarchical Clustering Algorithm is applied for multiple levels for accuracy. For tagging purpose POS tagger, porter stemmer is used. WordNet dictionary is utilized for determining the similarity by invoking the Jiang Conrath and Cosine similarity measure. Grouping is performed with respect to the highest similarity measure value with a mean threshold. This paper incorporates many parameters for finding similarity between words. In order to identify the disambiguated words, the sense identification is performed for the adjectives and comparison is performed. semcor and machine learning datasets are employed. On comparing with previous results for WSD, our work has improvised a lot which gives a percentage of 91.2%

scholarly journals An Enhanced Spectral Clustering Algorithm with S-Distance

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 596
Author(s):  
Krishna Kumar Sharma ◽  
Ayan Seal ◽  
Enrique Herrera-Viedma ◽  
Ondrej Krejcar
Keyword(s):  
Spectral Clustering ◽  
Clustering Algorithm ◽  
Spatial Clustering ◽  
Clustering Algorithms ◽  
Rank Test ◽  
Customer Churn ◽  
Signed Rank ◽  
Signed Rank Test ◽  
Spectral Clustering Algorithm ◽  
Industrial Databases

Calculating and monitoring customer churn metrics is important for companies to retain customers and earn more profit in business. In this study, a churn prediction framework is developed by modified spectral clustering (SC). However, the similarity measure plays an imperative role in clustering for predicting churn with better accuracy by analyzing industrial data. The linear Euclidean distance in the traditional SC is replaced by the non-linear S-distance (Sd). The Sd is deduced from the concept of S-divergence (SD). Several characteristics of Sd are discussed in this work. Assays are conducted to endorse the proposed clustering algorithm on four synthetics, eight UCI, two industrial databases and one telecommunications database related to customer churn. Three existing clustering algorithms—k-means, density-based spatial clustering of applications with noise and conventional SC—are also implemented on the above-mentioned 15 databases. The empirical outcomes show that the proposed clustering algorithm beats three existing clustering algorithms in terms of its Jaccard index, f-score, recall, precision and accuracy. Finally, we also test the significance of the clustering results by the Wilcoxon’s signed-rank test, Wilcoxon’s rank-sum test, and sign tests. The relative study shows that the outcomes of the proposed algorithm are interesting, especially in the case of clusters of arbitrary shape.

Research on Spectral Clustering

2014 ◽  
Vol 687-691 ◽  
pp. 1350-1353
Author(s):  
Li Li Fu ◽  
Yong Li Liu ◽  
Li Jing Hao
Keyword(s):  
Spectral Clustering ◽  
Clustering Algorithm ◽  
Theoretical Foundation ◽  
Clustering Algorithms ◽  
Spectral Graph Theory ◽  
Graph Partition ◽  
Mining Areas ◽  
Spectral Graph ◽  
Definition Of ◽  
Spectral Clustering Algorithm

Spectral clustering algorithm is a kind of clustering algorithm based on spectral graph theory. As spectral clustering has deep theoretical foundation as well as the advantage in dealing with non-convex distribution, it has received much attention in machine learning and data mining areas. The algorithm is easy to implement, and outperforms traditional clustering algorithms such as K-means algorithm. This paper aims to give some intuitions on spectral clustering. We describe different graph partition criteria, the definition of spectral clustering, and clustering steps, etc. Finally, in order to solve the disadvantage of spectral clustering, some improvements are introduced briefly.

scholarly journals A Clustering-Based Automatic Transfer Function Design for Volume Visualization

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Tianjin Zhang ◽  
Zongrui Yi ◽  
Jinta Zheng ◽  
Dong C. Liu ◽  
Wai-Mai Pang ◽  
...  
Keyword(s):  
Clustering Algorithm ◽  
Spatial Information ◽  
Transfer Functions ◽  
Clustering Algorithms ◽  
Volume Visualization ◽  
Automatic Generation ◽  
Volume Data ◽  
3D Volume ◽  
Ap Clustering ◽  
Different Tissues

The two-dimensional transfer functions (TFs) designed based on intensity-gradient magnitude (IGM) histogram are effective tools for the visualization and exploration of 3D volume data. However, traditional design methods usually depend on multiple times of trial-and-error. We propose a novel method for the automatic generation of transfer functions by performing the affinity propagation (AP) clustering algorithm on the IGM histogram. Compared with previous clustering algorithms that were employed in volume visualization, the AP clustering algorithm has much faster convergence speed and can achieve more accurate clustering results. In order to obtain meaningful clustering results, we introduce two similarity measurements: IGM similarity and spatial similarity. These two similarity measurements can effectively bring the voxels of the same tissue together and differentiate the voxels of different tissues so that the generated TFs can assign different optical properties to different tissues. Before performing the clustering algorithm on the IGM histogram, we propose to remove noisy voxels based on the spatial information of voxels. Our method does not require users to input the number of clusters, and the classification and visualization process is automatic and efficient. Experiments on various datasets demonstrate the effectiveness of the proposed method.

Kernel-Based Fuzzy Local Information Clustering Algorithm Self-integrating Non-Local Information

2021 ◽  
Author(s):  
Qiuyu Song ◽  
Chengmao Wu ◽  
Xiaoping Tian ◽  
Yue Song ◽  
Xiaokang Guo
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Integration Method ◽  
Clustering Algorithms ◽  
Noise Removal ◽  
Local Information ◽  
Gaussian Kernel ◽  
Distance Metric ◽  
High Noise ◽  
Non Local

Abstract The application of fuzzy clustering algorithms in image segmentation is a hot research topic nowadays. Existing fuzzy clustering algorithms have the following three problems: (1)The parameters of spatial information constraints can$'$t be selected adaptively; (2)The image corrupted by high noise can$'$t be segmented effectively; (3)It is difficult to achieve a balance between noise removal and detail preservation. In the fuzzy clustering based on the optimization model, the choice of distance metric is very important. Since the use of Euclidean distance will lead to sensitivity to outliers and noise, it is difficult to obtain satisfactory segmentation results, which will affect the clustering performance. This paper proposes an optimization algorithm based on the kernel-based fuzzy local information clustering integrating non-local information (KFLNLI). The algorithm adopts a self-integration method to introduce local and non-local information of images, which solves the common problems of current clustering algorithm. Firstly, the self-integration method solves the problem of selecting spatial constraint parameters. The algorithm uses continuous self-learning iteration to calculate the weight coefficients; Secondly, the distance metric uses Gaussian kernel function to induce the distance to further enhance the robustness against noise and the adaptivity of processing different images; Finally, both local and non-local information are introduced to achieve a segmentation effect that can eliminate most of the noise and retain the original details of the image. Experimental results show that the algorithm is superior to existing state-of-the-art fuzzy clustering-related algorithm in the presence of high noise.

SeqPAM

2008 ◽  
pp. 17-38 ◽  
Author(s):  
Pradeep Kumar Kumar ◽  
Raju S. Bapi ◽  
P. Radha Krishna
Keyword(s):  
Similarity Measure ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Similarity Measures ◽  
Sequential Data ◽  
Cluster Validation ◽  
Web Personalization ◽  
Similarity Preserving ◽  
Validation Technique ◽  
The Web

With the growth in the number of web users and necessity for making information available on the web, the problem of web personalization has become very critical and popular. Developers are trying to customize a web site to the needs of specific users with the help of knowledge acquired from user navigational behavior. Since user page visits are intrinsically sequential in nature, efficient clustering algorithms for sequential data are needed. In this paper, we introduce a similarity preserving function called sequence and set similarity measure S3M that captures both the order of occurrence of page visits as well as the content of pages. We conducted pilot experiments comparing the results of PAM, a standard clustering algorithm, with two similarity measures: Cosine and S3M. The goodness of the clusters resulting from both the measures was computed using a cluster validation technique based on average levensthein distance. Results on pilot dataset established the effectiveness of S3M for sequential data. Based on these results, we proposed a new clustering algorithm, SeqPAM for clustering sequential data. We tested the new algorithm on two datasets namely, cti and msnbc datasets. We provided recommendations for web personalization based on the clusters obtained from SeqPAM for msnbc dataset.

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