Zhihong Ouyang^*, Lei Xue, Feng Ding, Yongsheng Duan

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 983-1008, 2023, DOI:10.32604/cmc.2023.042222 - 31 October 2023

Abstract Affinity propagation (AP) is a widely used exemplar-based clustering approach with superior efficiency and clustering quality. Nevertheless, a common issue with AP clustering is the presence of excessive exemplars, which limits its ability to perform effective aggregation. This research aims to enable AP to automatically aggregate to produce fewer and more compact clusters, without changing the similarity matrix or customizing preference parameters, as done in existing enhanced approaches. An automatic aggregation enhanced affinity propagation (AAEAP) clustering algorithm is proposed, which combines a dependable partitioning clustering approach with AP to achieve this purpose. The partitioning clustering… More >

Zhenqiu Xiao^1,*, Bin Yang², Desy Tjahjadi³

CMC-Computers, Materials & Continua, Vol.60, No.3, pp. 1141-1154, 2019, DOI:10.32604/cmc.2019.05638

Abstract Crowd counting is a challenging task in crowded scenes due to heavy occlusions, appearance variations and perspective distortions. Current crowd counting methods typically operate on an image patch level with overlaps, then sum over the patches to get the final count. In this paper we describe a real-time pedestrian counting framework based on a two-stage human detection algorithm. Existing works with overhead cameras is mainly based on visual tracking, and their robustness is rather limited. On the other hand, some works, which focus on improving the performances, are too complicated to be realistic. By adopting… More >

Ruikang Xing^1,*, Chenghai Li¹

CMC-Computers, Materials & Continua, Vol.60, No.2, pp. 767-780, 2019, DOI:10.32604/cmc.2019.04500

Abstract In clustering analysis, the key to deciding clustering quality is to determine the optimal number of clusters. At present, most clustering algorithms need to give the number of clusters in advance for clustering analysis of the samples. How to gain the correct optimal number of clusters has been an important topic of clustering validation study. By studying and analyzing the FCM algorithm in this study, an accurate and efficient algorithm used to confirm the optimal number of clusters is proposed for the defects of traditional FCM algorithm. For time and clustering accuracy problems of FCM More >