scholarly journals Superpixel Segmentation Based on Grid Point Density Peak Clustering

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6374
Author(s):  
Xianyi Chen ◽  
Xiafu Peng ◽  
Sun’an Wang
Keyword(s):  
Grid Point ◽  
Detection Methods ◽  
Feature Points ◽  
Good Adhesion ◽  
Density Peak ◽  
Superpixel Segmentation ◽  
Density Peaks ◽  
Segmentation Accuracy ◽  
Point Density ◽  
Density Peak Clustering

Superpixel segmentation is one of the key image preprocessing steps in object recognition and detection methods. However, the over-segmentation in the smoothly connected homogenous region in an image is the key problem. That would produce redundant complex jagged textures. In this paper, the density peak clustering will be used to reduce the redundant superpixels and highlight the primary textures and contours of the salient objects. Firstly, the grid pixels are extracted as feature points, and the density of each feature point will be defined. Secondly, the cluster centers are extracted with the density peaks. Finally, all the feature points will be clustered by the density peaks. The pixel blocks, which are obtained by the above steps, are superpixels. The method is carried out in the BSDS500 dataset, and the experimental results show that the Boundary Recall (BR) and Achievement Segmentation Accuracy (ASA) are 95.0% and 96.3%, respectively. In addition, the proposed method has better performance in efficiency (30 fps). The comparison experiments show that not only do the superpixel boundaries have good adhesion to the primary textures and contours of the salient objects, but they can also effectively reduce the redundant superpixels in the homogeneous region.

scholarly journals Improving Density Peak Clustering by Automatic Peak Selection and Single Linkage Clustering

Symmetry ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1168
Author(s):  
Jun-Lin Lin ◽  
Jen-Chieh Kuo ◽  
Hsing-Wang Chuang
Keyword(s):  
Clustering Algorithm ◽  
Academic Community ◽  
Performance Study ◽  
Potential Density ◽  
Cluster Assignment ◽  
Density Peak ◽  
Single Linkage ◽  
Density Peaks ◽  
Assignment Strategy ◽  
Density Peak Clustering

Density peak clustering (DPC) is a density-based clustering method that has attracted much attention in the academic community. DPC works by first searching density peaks in the dataset, and then assigning each data point to the same cluster as its nearest higher-density point. One problem with DPC is the determination of the density peaks, where poor selection of the density peaks could yield poor clustering results. Another problem with DPC is its cluster assignment strategy, which often makes incorrect cluster assignments for data points that are far from their nearest higher-density points. This study modifies DPC and proposes a new clustering algorithm to resolve the above problems. The proposed algorithm uses the radius of the neighborhood to automatically select a set of the likely density peaks, which are far from their nearest higher-density points. Using the potential density peaks as the density peaks, it then applies DPC to yield the preliminary clustering results. Finally, it uses single-linkage clustering on the preliminary clustering results to reduce the number of clusters, if necessary. The proposed algorithm avoids the cluster assignment problem in DPC because the cluster assignments for the potential density peaks are based on single-linkage clustering, not based on DPC. Our performance study shows that the proposed algorithm outperforms DPC for datasets with irregularly shaped clusters.

scholarly journals A Fast Density Peak Clustering Method with Autoselect Cluster Centers

2022 ◽  
Vol 2022 ◽  
pp. 1-13
Author(s):  
Zhihe Wang ◽  
Yongbiao Li ◽  
Hui Du ◽  
Xiaofen Wei
Keyword(s):  
Experimental Results ◽  
Cluster Center ◽  
Clustering Method ◽  
Density Peak ◽  
Density Peaks ◽  
Density Peaks Clustering ◽  
Manual Selection ◽  
Density Peak Clustering ◽  
Selection Of

Aiming at density peaks clustering needs to manually select cluster centers, this paper proposes a fast new clustering method with auto-select cluster centers. Firstly, our method groups the data and marks each group as core or boundary groups according to its density. Secondly, it determines clusters by iteratively merging two core groups whose distance is less than the threshold and selects the cluster centers at the densest position in each cluster. Finally, it assigns boundary groups to the cluster corresponding to the nearest cluster center. Our method eliminates the need for the manual selection of cluster centers and improves clustering efficiency with the experimental results.

HaloDPC: An Improved Recognition Method on Halo Node for Density Peak Clustering Algorithm

2019 ◽  
Vol 33 (08) ◽  
pp. 1950012 ◽  
Author(s):  
Jianhua Jiang ◽  
Wei Zhou ◽  
Limin Wang ◽  
Xin Tao ◽  
Keqin Li
Keyword(s):  
Clustering Algorithm ◽  
High Dimensionality ◽  
Test Cases ◽  
Low Density ◽  
Recognition Method ◽  
Density Peak ◽  
Irregular Shapes ◽  
Density Peaks ◽  
Density Peaks Clustering ◽  
Density Peak Clustering

The density peaks clustering (DPC) is known as an excellent approach to detect some complicated-shaped clusters with high-dimensionality. However, it is not able to detect outliers, hub nodes and boundary nodes, or form low-density clusters. Therefore, halo is adopted to improve the performance of DPC in processing low-density nodes. This paper explores the potential reasons for adopting halos instead of low-density nodes, and proposes an improved recognition method on Halo node for Density Peak Clustering algorithm (HaloDPC). The proposed HaloDPC has improved the ability to deal with varying densities, irregular shapes, the number of clusters, outlier and hub node detection. This paper presents the advantages of the HaloDPC algorithm on several test cases.

scholarly journals Self-Training Algorithm Based on Density Peaks Combining Globally Adaptive Multi-Local Noise Filter

2021 ◽  
Author(s):  
Shuaijun Li ◽  
Jia Lu
Keyword(s):  
Data Sets ◽  
Training Algorithm ◽  
Real World Data ◽  
Density Peak ◽  
Data Set ◽  
Density Peaks ◽  
Noise Filter ◽  
Density Peak Clustering ◽  
A Current ◽  
Noise Filters

Abstract Self-training algorithm can quickly train an supervised classifier through a few labeled samples and lots of unlabeled samples. However, self-training algorithm is often affected by mislabeled samples, and local noise filter is proposed to detect the mislabeled samples. Nevertheless, current local noise filters have the problems: (a) Current local noise filters ignore the spatial distribution of the nearest neighbors in different classes. (b) They can’t perform well when mislabeled samples are located in the overlapping areas of different classes. To solve the above challenges, a new self-training algorithm based on density peaks combining globally adaptive multi-local noise filter (STDP-GAMNF) is proposed. Firstly, the spatial structure of data set is revealed by density peak clustering, and it is used for helping self-training to label unlabeled samples. In the meantime, after each epoch of labeling, GAMLNF can comprehensively judge whether a sample is a mislabeled sample from multiple classes or not, and will reduce the influence of edge samples effectively. The corresponding experimental results conducted on eighteen real-world data sets demonstrate that GAMLNF is not sensitive to the value of the neighbor parameter k, and it can be adaptive to find the appropriate number of neighbors of each class.

scholarly journals Three-Dimensional Asphalt Pavement Crack Detection Based on Fruit Fly Optimisation Density Peak Clustering

2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
Wei Li ◽  
Ranran Deng ◽  
Yingjie Zhang ◽  
Zhaoyun Sun ◽  
Xueli Hao ◽  
...  
Keyword(s):  
Crack Detection ◽  
Clustering Algorithm ◽  
Asphalt Pavement ◽  
Fruit Fly ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Density Peak ◽  
3D Data ◽  
Density Peak Clustering ◽  
Pavement Crack Detection

Complex pavement texture and noise impede the effectiveness of existing 3D pavement crack detection methods. To improve pavement crack detection accuracy, we propose a 3D asphalt pavement crack detection algorithm based on fruit fly optimisation density peak clustering (FO-DPC). Firstly, the 3D data of asphalt pavement are collected, and a 3D image acquisition system is built using Gocator3100 series binocular intelligent sensors. Then, the fruit fly optimisation algorithm is adopted to improve the density peak clustering algorithm. Clustering analysis that can accurately detect cracks is performed on the height characteristics of the 3D data of the asphalt pavement. Finally, the clustering results are projected onto a 2D space and compared with the results of other 2D crack detection methods. Following this comparison, it is established that the proposed algorithm outperforms existing methods in detecting asphalt pavement cracks.

scholarly journals Density Peak Clustering Based on Relative Density Optimization

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Chunzhong Li ◽  
Yunong Zhang
Keyword(s):  
Relative Density ◽  
Clustering Algorithms ◽  
Real Data ◽  
Classification Problem ◽  
Data Sets ◽  
Density Peak ◽  
Data Set ◽  
Density Peaks ◽  
Assignment Strategy ◽  
Density Peak Clustering

Among numerous clustering algorithms, clustering by fast search and find of density peaks (DPC) is favoured because it is less affected by shapes and density structures of the data set. However, DPC still shows some limitations in clustering of data set with heterogeneity clusters and easily makes mistakes in assignment of remaining points. The new algorithm, density peak clustering based on relative density optimization (RDO-DPC), is proposed to settle these problems and try obtaining better results. With the help of neighborhood information of sample points, the proposed algorithm defines relative density of the sample data and searches and recognizes density peaks of the nonhomogeneous distribution as cluster centers. A new assignment strategy is proposed to solve the abundance classification problem. The experiments on synthetic and real data sets show good performance of the proposed algorithm.

scholarly journals Accelerating Density Peak Clustering Algorithm

Symmetry ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 859 ◽  
Author(s):  
Lin
Keyword(s):  
Clustering Algorithm ◽  
Local Density ◽  
Early Stage ◽  
Separation Distance ◽  
Density Peak ◽  
Density Peaks ◽  
Density Based Clustering ◽  
Data Points ◽  
Data Point ◽  
Density Peak Clustering

The Density Peak Clustering (DPC) algorithm is a new density-based clustering method. It spends most of its execution time on calculating the local density and the separation distance for each data point in a dataset. The purpose of this study is to accelerate its computation. On average, the DPC algorithm scans half of the dataset to calculate the separation distance of each data point. We propose an approach to calculate the separation distance of a data point by scanning only the neighbors of the data point. Additionally, the purpose of the separation distance is to assist in choosing the density peaks, which are the data points with both high local density and high separation distance. We propose an approach to identify non-peak data points at an early stage to avoid calculating their separation distances. Our experimental results show that most of the data points in a dataset can benefit from the proposed approaches to accelerate the DPC algorithm.

scholarly journals A Weakly Supervised Gas-Path Anomaly Detection Method for Civil Aero-Engines Based on Mapping Relationship Mining of Gas-Path Parameters and Improved Density Peak Clustering

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4526
Author(s):  
Hao Sun ◽  
Xuyun Fu ◽  
Shisheng Zhong
Keyword(s):  
Anomaly Detection ◽  
Loss Function ◽  
Detection Method ◽  
Detection Methods ◽  
Density Peak ◽  
Parameter Adjustment ◽  
Weakly Supervised ◽  
Predicted Values ◽  
Density Peak Clustering ◽  
Aero Engines

Gas-path anomalies account for more than 90% of all civil aero-engine anomalies. It is essential to develop accurate gas-path anomaly detection methods. Therefore, a weakly supervised gas-path anomaly detection method for civil aero-engines based on mapping relationship mining of gas-path parameters and improved density peak clustering is proposed. First, the encoder-decoder, composed of an attention mechanism and a long short-term memory neural network, is used to construct the mapping relationship mining model among gas-path parameters. The predicted values of gas-path parameters under the restriction of mapping relationships are obtained. The deviation degree from the original values to the predicted values is regarded as the feature. To force the extracted features to better reflect the anomalies and make full use of weakly supervised labels, a weakly supervised cross-entropy loss function under extreme class imbalance is deployed. This loss function can be combined with a simple classifier to significantly improve the feature extraction results, in which anomaly samples are more different from normal samples and do not reduce the mining precision. Finally, an anomaly detection method is deployed based on improved density peak clustering and a weakly supervised clustering parameter adjustment strategy. In the improved density peak clustering method, the local density is enhanced by K-nearest neighbors, and the clustering effect is improved by a new outlier threshold determination method and a new outlier treatment method. Through these settings, the accuracy of dividing outliers and clustering can be improved, and the influence of outliers on the clustering process reduced. By introducing weakly supervised label information and automatically iterating according to clustering and anomaly detection results to update the hyperparameter settings, a weakly supervised anomaly detection method without complex parameter adjustment processes can be implemented. The experimental results demonstrate the superiority of the proposed method.

scholarly journals Density Peak Clustering Algorithm Considering Topological Features

Electronics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 459
Author(s):  
Shuyi Lu ◽  
Yuanjie Zheng ◽  
Rong Luo ◽  
Weikuan Jia ◽  
Jian Lian ◽  
...  
Keyword(s):  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Original Data ◽  
Power Law Distribution ◽  
Density Peak ◽  
Topological Features ◽  
Density Peaks ◽  
Topological Characteristics ◽  
Density Peak Clustering ◽  
Clustering Data

The clustering algorithm plays an important role in data mining and image processing. The breakthrough of algorithm precision and method directly affects the direction and progress of the following research. At present, types of clustering algorithms are mainly divided into hierarchical, density-based, grid-based and model-based ones. This paper mainly studies the Clustering by Fast Search and Find of Density Peaks (CFSFDP) algorithm, which is a new clustering method based on density. The algorithm has the characteristics of no iterative process, few parameters and high precision. However, we found that the clustering algorithm did not consider the original topological characteristics of the data. We also found that the clustering data is similar to the social network nodes mentioned in DeepWalk, which satisfied power-law distribution. In this study, we tried to consider the topological characteristics of the graph in the clustering algorithm. Based on previous studies, we propose a clustering algorithm that adds the topological characteristics of original data on the basis of the CFSFDP algorithm. Our experimental results show that the clustering algorithm with topological features significantly improves the clustering effect and proves that the addition of topological features is effective and feasible.

scholarly journals The Tessellation-Level-Tree: characterizing the nested hierarchy of density peaks and their spatial distribution in cosmological N-body simulations

2020 ◽  
Vol 493 (4) ◽  
pp. 5693-5712 ◽  
Author(s):  
Philipp Busch ◽  
Simon D M White
Keyword(s):  
Local Density ◽  
Voronoi Tessellation ◽  
Density Peak ◽  
Hierarchical Tree ◽  
Density Peaks ◽  
External Point ◽  
Point Density ◽  
Nested Hierarchy ◽  
Density Threshold ◽  
Hierarchical Tree Structure

ABSTRACT We use the Millennium and Millennium-II simulations to illustrate the Tessellation-Level-Tree  (tlt), a hierarchical tree structure linking density peaks in a field constructed by voronoi tessellation of the particles in a cosmological N-body simulation. The tlt uniquely partitions the simulation particles into disjoint subsets, each associated with a local density peak. Each peak is a subpeak of a unique higher peak. The tlt can be persistence filtered to suppress peaks produced by discreteness noise. Thresholding a peak’s particle list at $\sim 80\left \langle \rho \right \rangle \,$ results in a structure similar to a standard friend-of-friends halo and its subhaloes. For thresholds below $\sim 7\left \langle \rho \right \rangle \,$, the largest structure percolates and is much more massive than other objects. It may be considered as defining the cosmic web. For a threshold of $5\left \langle \rho \right \rangle \,$, it contains about half of all cosmic mass and occupies $\sim 1{{\ \rm per\ cent}}$ of all cosmic volume; a typical external point is then ∼7h−1 Mpc from the web. We investigate the internal structure and clustering of tlt peaks. Defining the saddle point density ρlim  as the density at which a peak joins its parent peak, we show the median value of ρlim  for FoF-like peaks to be similar to the density threshold at percolation. Assembly bias as a function of ρlim  is stronger than for any known internal halo property. For peaks of group mass and below, the lowest quintile in ρlim  has b ≈ 0, and is thus uncorrelated with the mass distribution.

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