A New Method for Grayscale Image Segmentation Based on Affinity Propagation Clustering Algorithm

2013 ◽  
Author(s):  
Hui Du ◽  
Yuping Wang ◽  
Lili Duan
Keyword(s):  
Image Segmentation ◽  
Clustering Algorithm ◽  
New Method ◽  
Affinity Propagation ◽  
Grayscale Image ◽  
Affinity Propagation Clustering

Affinity Propagation Clustering Algorithm Based on PCA

2014 ◽  
Vol 590 ◽  
pp. 688-692
Author(s):  
Bei Chen ◽  
Kun Song
Keyword(s):  
Clustering Algorithm ◽  
Dimensional Space ◽  
Original Data ◽  
New Method ◽  
Affinity Propagation ◽  
High Dimensional ◽  
Redundant Information ◽  
Affinity Propagation Clustering ◽  
Components Analysis ◽  
Low Dimensional

Overlap information usually exits in the high-dimensional data. Misclassified points may be more when affinity propagation clustering is applied to these data. Concerning this problem, a new method combining principal components analysis and affinity propagation clustering is proposed. In this method, dimensionality of the original data is reduced on the premise of reserving most information of the variables. Then, affinity propagation clustering is implemented in the low-dimensional space. Thus, because the redundant information is deleted, the classification is accurate. Experiment is done by using this new method, the results of the experiment explain that this method is effective.

Clustering web search results using Wikipedia resource

2020 ◽  
Vol 0 (10/2019) ◽  
pp. 25-29
Author(s):  
Chung Tran ◽  
Andrzej Ameljańczyk
Keyword(s):  
Clustering Algorithm ◽  
Web Search ◽  
New Method ◽  
Affinity Propagation ◽  
Search Results ◽  
Knowledge Resource ◽  
Affinity Propagation Clustering ◽  
Popular Knowledge ◽  
Global Performance ◽  
Clustering Search

The paper presents a proposal of a new method for clustering search results. The method uses an external knowledge resource, which can be, for example, Wikipedia. Wikipedia – the largest encyclopedia, is a free and popular knowledge resource which is used to extract topics from short texts. Similarities between documents are calculated based on the similarities between these topics. After that, affinity propagation clustering algorithm is employed to cluster web search results. Proposed method is tested by AMBIENT dataset and evaluated within the experimental framework provided by a SemEval-2013 task. The paper also suggests new method to compare global performance of algorithms using multi – criteria analysis.

Texture Image Segmentation Using Affinity Propagation and Spectral Clustering

2015 ◽  
Vol 29 (05) ◽  
pp. 1555009 ◽  
Author(s):  
Hui Du ◽  
Yuping Wang ◽  
Xiaopan Dong
Keyword(s):  
Image Segmentation ◽  
Spectral Clustering ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Computational Cost ◽  
Affinity Propagation ◽  
Texture Image ◽  
Texture Image Segmentation ◽  
Two Phases ◽  
Representative Points

Clustering is a popular and effective method for image segmentation. However, existing cluster methods often suffer the following problems: (1) Need a huge space and a lot of computation when the input data are large. (2) Need to assign some parameters (e.g. number of clusters) in advance which will affect the clustering results greatly. To save the space and computation, reduce the sensitivity of the parameters, and improve the effectiveness and efficiency of the clustering algorithms, we construct a new clustering algorithm for image segmentation. The new algorithm consists of two phases: coarsening clustering and exact clustering. First, we use Affinity Propagation (AP) algorithm for coarsening. Specifically, in order to save the space and computational cost, we only compute the similarity between each point and its t nearest neighbors, and get a condensed similarity matrix (with only t columns, where t << N and N is the number of data points). Second, to further improve the efficiency and effectiveness of the proposed algorithm, the Self-tuning Spectral Clustering (SSC) is used to the resulted points (the representative points gotten in the first phase) to do the exact clustering. As a result, the proposed algorithm can quickly and precisely realize the clustering for texture image segmentation. The experimental results show that the proposed algorithm is more efficient than the compared algorithms FCM, K-means and SOM.

Feature Selection of Sudden Failure Based on Affinity Propagation Clustering

2012 ◽  
Vol 586 ◽  
pp. 241-246
Author(s):  
Li Min Li ◽  
Zhong Sheng Wang
Keyword(s):  
Feature Selection ◽  
Clustering Algorithm ◽  
Feature Space ◽  
Failure Diagnosis ◽  
Affinity Propagation ◽  
Svm Classifier ◽  
Incorrect Answer ◽  
Sudden Failure ◽  
Affinity Propagation Clustering ◽  
Selection Of

When diagnosing sudden mechanical failure, in order to make the result of classification more accurate, in this article we describe an affinity propagation clustering algorithm for feature selection of sudden machinery failure diagnosis. General methods of feature selection select features by reducing dimension of the features, at the same time changing the data in the feature space, which would result in incorrect answer to the diagnosis. While affinity propagation method is based on measuring similarity between features whereby redundancy therein is removed, and selecting the exemplar subset of features, while doesn't change the data in the feature space. After testing on clustering and taking the result of PCA and affinity propagation clustering as input of a same SVM classifier, we get the conclusion that the latter has lower error than the former.

Sign in / Sign up

Export Citation Format

Share Document