scholarly journals Do tracking by clustering anchors output from region proposal network

2018 ◽  
Vol 246 ◽  
pp. 03006
Author(s):  
Yanke Wang ◽  
Qidan Zhu ◽  
Wenchang Nie ◽  
Hong Xiao
Keyword(s):  
Clustering Algorithms ◽  
Mean Shift ◽  
Computational Cost ◽  
Feature Space ◽  
Experimental Results ◽  
Similarity Function ◽  
Density Peak ◽  
Training Images ◽  
Density Peak Clustering ◽  
Fully Connected

Most existing clustering algorithms suffer from the computation of similarity function and the representation of each object. In this paper, we propose a clustering tracker based on region proposal network (RPN-C) to do tracking by clustering anchors output by region proposal network into potential centers. We first cut off the second part of Faster RCNN and then cast clustering algorithms in feature space of anchors, including K-Means, mean shift and density peak clustering strategy in terms of anchors’ centroid and scale information. Without fully connected layers, the RPN-C tracker can lower the computational cost up to 60% and still, it can effectively maintain an accurate prediction for the localization in next frame. To evaluate the robustness of this tracker, we establish a dataset containing over 2000 training images and 7 testing sequences of 8 kinds of fruits. The experimental results on our own datasets demonstrate that the proposed tracker performs excellently both in location of object and the decision of scale and has a strong advantage of stability in the context of occlusion and complicated background.

scholarly journals VDPC: Variational Density Peak Clustering Algorithm

2021 ◽  
Author(s):  
Yizhang Wang ◽  
Di Wang ◽  
You Zhou ◽  
Chai Quek ◽  
Xiaofeng Zhang
Keyword(s):  
Clustering Algorithm ◽  
Cluster Formation ◽  
Clustering Algorithms ◽  
Data Distribution ◽  
Distribution Patterns ◽  
Clustering Methods ◽  
Density Peak ◽  
Global Parameter ◽  
Density Peak Clustering ◽  
Parameter Values

<div>Clustering is an important unsupervised knowledge acquisition method, which divides the unlabeled data into different groups \cite{atilgan2021efficient,d2021automatic}. Different clustering algorithms make different assumptions on the cluster formation, thus, most clustering algorithms are able to well handle at least one particular type of data distribution but may not well handle the other types of distributions. For example, K-means identifies convex clusters well \cite{bai2017fast}, and DBSCAN is able to find clusters with similar densities \cite{DBSCAN}. </div><div>Therefore, most clustering methods may not work well on data distribution patterns that are different from the assumptions being made and on a mixture of different distribution patterns. Taking DBSCAN as an example, it is sensitive to the loosely connected points between dense natural clusters as illustrated in Figure~\ref{figconnect}. The density of the connected points shown in Figure~\ref{figconnect} is different from the natural clusters on both ends, however, DBSCAN with fixed global parameter values may wrongly assign these connected points and consider all the data points in Figure~\ref{figconnect} as one big cluster.</div>

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.

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 VDPC: Variational Density Peak Clustering Algorithm

2021 ◽  
Author(s):  
Yizhang Wang ◽  
Di Wang ◽  
You Zhou ◽  
Chai Quek ◽  
Xiaofeng Zhang
Keyword(s):  
Clustering Algorithm ◽  
Cluster Formation ◽  
Clustering Algorithms ◽  
Data Distribution ◽  
Distribution Patterns ◽  
Clustering Methods ◽  
Density Peak ◽  
Global Parameter ◽  
Density Peak Clustering ◽  
Parameter Values

<div>Clustering is an important unsupervised knowledge acquisition method, which divides the unlabeled data into different groups \cite{atilgan2021efficient,d2021automatic}. Different clustering algorithms make different assumptions on the cluster formation, thus, most clustering algorithms are able to well handle at least one particular type of data distribution but may not well handle the other types of distributions. For example, K-means identifies convex clusters well \cite{bai2017fast}, and DBSCAN is able to find clusters with similar densities \cite{DBSCAN}. </div><div>Therefore, most clustering methods may not work well on data distribution patterns that are different from the assumptions being made and on a mixture of different distribution patterns. Taking DBSCAN as an example, it is sensitive to the loosely connected points between dense natural clusters as illustrated in Figure~\ref{figconnect}. The density of the connected points shown in Figure~\ref{figconnect} is different from the natural clusters on both ends, however, DBSCAN with fixed global parameter values may wrongly assign these connected points and consider all the data points in Figure~\ref{figconnect} as one big cluster.</div>

scholarly journals Clustering with Missing Features: A Density-Based Approach

Symmetry ◽  
2022 ◽  
Vol 14 (1) ◽  
pp. 60
Author(s):  
Kun Gao ◽  
Hassan Ali Khan ◽  
Wenwen Qu
Keyword(s):  
Real World ◽  
Incomplete Data ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Distance Matrix ◽  
Density Peak ◽  
Density Clustering ◽  
Real World Datasets ◽  
Density Peak Clustering ◽  
Feature Values

Density clustering has been widely used in many research disciplines to determine the structure of real-world datasets. Existing density clustering algorithms only work well on complete datasets. In real-world datasets, however, there may be missing feature values due to technical limitations. Many imputation methods used for density clustering cause the aggregation phenomenon. To solve this problem, a two-stage novel density peak clustering approach with missing features is proposed: First, the density peak clustering algorithm is used for the data with complete features, while the labeled core points that can represent the whole data distribution are used to train the classifier. Second, we calculate a symmetrical FWPD distance matrix for incomplete data points, then the incomplete data are imputed by the symmetrical FWPD distance matrix and classified by the classifier. The experimental results show that the proposed approach performs well on both synthetic datasets and real datasets.

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 Dynamic Group Recommendation Based on the Attention Mechanism

2019 ◽  
Vol 11 (9) ◽  
pp. 198 ◽  
Author(s):  
Haiyan Xu ◽  
Yanhui Ding ◽  
Jing Sun ◽  
Kun Zhao ◽  
Yuanjian Chen
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Attention Mechanism ◽  
Experimental Results ◽  
Group Recommendation ◽  
Density Peak ◽  
Significant Research ◽  
Group Members ◽  
Density Peak Clustering

Group recommendation has attracted significant research efforts for its importance in benefiting group members. The purpose of group recommendation is to provide recommendations to group users, such as recommending a movie to several friends. Group recommendation requires that the recommendation should be as satisfactory as possible to each member of the group. Due to the lack of weighting of users in different items, group decision-making cannot be made dynamically. Therefore, in this paper, a dynamic recommendation method based on the attention mechanism is proposed. Firstly, an improved density peak clustering ( DPC ) algorithm is used to discover the potential group; and then the attention mechanism is adopted to learn the influence weight of each user. The normalized discounted cumulative gain (NDCG) and hit ratio (HR) are adopted to evaluate the validity of the recommendation results. Experimental results on the CAMRa2011 dataset show that our method is effective.

scholarly journals A Partial Discharge Localization Method in Transformers Based on Linear Conversion and Density Peak Clustering

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Shudong Wang ◽  
Yigang He ◽  
Baiqiang Yin ◽  
Wenbo Zeng ◽  
Ying Deng ◽  
...  
Keyword(s):  
Partial Discharge ◽  
Density Peak ◽  
Localization Method ◽  
Linear Conversion ◽  
Density Peak Clustering

scholarly journals Intradialytic blood pressure pattern recognition based on density peak clustering

2018 ◽  
Vol 83 ◽  
pp. 33-39 ◽  
Author(s):  
Feng Wang ◽  
Jing-yi Zhou ◽  
Yu Tian ◽  
Yu Wang ◽  
Ping Zhang ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Pattern Recognition ◽  
Density Peak ◽  
Pressure Pattern ◽  
Density Peak Clustering
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