An Underwater Image Classification Algorithm Based on PCA and D-S Evidence Theory

摘要：

　　In this paper,an integrated underwater image classification method is proposed by combing D-S(Dempster-Shafer)evidence theory and Principal Component Analysis(PCA)method.First,underwater image texture features are extracted and compressed by using PCA method.Then,the correlation coefficient between image feature and classification pattern is calculated,and the fusion belief function of every image classification pattern can be obtained through D-S evidence theory.Finally,by comparing the results between single PCA feature components and fusion data recognition,the superiority of the proposed integrated method in image recognition is demonstrated.

关键词： D-S evidence theory PCA Texture feature extraction Belief function Image classification

作者: Yihua Shi Daqi Zhu

作者单位: Laboratory of Underwater Vehicles and Intelligent Systems,Shanghai Maritime University,Haigang Avenue 1550,Pudong New District,Shanghai 201306,China

会议类型: 国际会议

会议名称: The 2015 Chinese Intelligent Automation Conference(2015中国智能自动化会议)

会议地点: 福州

会议语种:英文

页码: 541-549

在线出版日期: 2015-05-08（万方平台首次上网日期，不代表论文的发表时间）