Robust Suppressed Competitive Picture Fuzzy Clustering Driven by Entropy
- PDF / 7,553,195 Bytes
- 27 Pages / 595.276 x 790.866 pts Page_size
- 38 Downloads / 215 Views
Robust Suppressed Competitive Picture Fuzzy Clustering Driven by Entropy Chengmao Wu1 • Na Liu1
Received: 20 August 2019 / Revised: 28 July 2020 / Accepted: 6 August 2020 Ó Taiwan Fuzzy Systems Association 2020
Abstract In the fuzzy clustering process, the clustering number for image or text data to be classified is not easy to determine or unknown. The competitive learning algorithm can automatically determine the optimal clustering number to avoid the problem of inappropriate artificial selection. In this paper, based on the clustering by competitive agglomeration (CA), the idea of the ‘‘Competitive Learning Mechanism’’ is introduced to picture fuzzy clustering to obtain the competitive agglomeration picture fuzzy clustering (CAPFCM). The competitive learning regular term of the CAPFCM objective function is reinterpreted from the perspective of minimizing the entropy, and the general framework of the entropy competitive clustering algorithm is constructed. Moreover, the competitive learning regular term of the objective function is replaced by quadratic entropy, Renyi entropy or Shannon entropy to obtain different entropy competitive clustering. To improve the efficiency of the CAPFCM algorithm, the suppressed factor is introduced to appropriately increase the maximum value of the picture fuzzy partition information for different clusters and suppress all others. In addition, this paper proposes a robust adaptive entropy competitive picture fuzzy clustering segmentation algorithm with neighborhood spatial information constraints to enhance the antinoise ability of the picture fuzzy clustering algorithm for noise image. Experiments show that robust CAPFCM can automatically determine the clustering number and greatly & Na Liu [email protected] Chengmao Wu [email protected] 1
School of Electronic Engineering, Xi’an University of Posts and Telecommunications, Xi’an 710121, China
improve the operation efficiency and segmentation performance. Keywords Competitive learning Entropy competitive clustering Suppressed factor Spatial neighborhood information
1 Introduction Clustering is a process of grouping and classifying things according to certain requirements and rules. As the main way of unsupervised identification, clustering only relies on the similarity between things as classification standard without any prior knowledge about categories [1]. At present, the commonly used clustering methods can be roughly classified into two categories: hierarchical method and partition-based method [2]. The hierarchical method is to decompose a given data set according to the hierarchy until some set condition is satisfied. This method can be divided into splitting method and condensation method. The splitting method first classifies all the data into the same cluster, and then gradually subdivides them into smaller clusters. The total number of clusters is increasing; the method of condensation is the opposite. This method first classifies each sample into a cluster, and then gradually classifies the sample satisfy
Data Loading...
