Combining Frequent Itemsets and Statistical Features for Texture Classification in Relative Phase Domain

摘要：

　　relative phase is a newly developing technology for extracting features of images from phase domain and this paper studies a method of texture classification in relative phase domain.Because relative phase information can be obtained only in complex wavelet,we select DTCWT(Dual Tree Complex Wavelet Transform)and PDTDFB(Pyramidal Dual Tree Directional Filter Bank)to decompose images into different subbands at different levels and directions,and then the wavelet coefficients are mapped into relative phase domain.In relative phase domain,we calculate the frequent 2-itemsets and statistical characteristics mean and standard deviation of each subband as image features for texture classification.The experimental results show that our texture classification method has better performance in relative phase domain built from either DTCWT or PDTDFB.

关键词： texture classification relative phase DTCWT PDTDFB frequent 2-itemset statistical characteristic

作者: Li Liu Chen Chen Longfei Yang

作者单位: School of Information Science and Engineering Lanzhou University Lanzhou,China

会议类型: 国际会议

会议名称: The 2014 10th International Conference on Natural Computation (ICNC 2014) and the 2014 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2014)(第十届自然计算和第十一届模糊系统与知识发现国际会议)

会议地点: 厦门

会议语种:英文

页码: 387-392

在线出版日期: 2014-08-19（万方平台首次上网日期，不代表论文的发表时间）