Texture Image Retrieval Algorithm with Dual Tree Complex Contourlet and Three Statistical Features
Texture image retrieval system using contourlet transform has better performance than the same structure system based on wavelet transform due to contourlets better directional information representation than wavelet transform. In order to improve the retrieval rate further, a dual-tree complex contourlet transform based texture image retrieval system was proposed in this paper. In the system, the dual tree contourlet transform was used to transform each image into contourlet domain and implemented multiscale decomposition, sub-bands energy, standard deviations and skewness in contourlet domain were cascaded to form feature vectors, and the similarity metric used here is Canberra distance. Experimental results on brodatz test images set show that dual tree contourlet transform based image retrieval system is superior to those of the original contourlet transform, nonsubsampled contourlet transform under the same system structure with almost same length of feature vectors, retrieval time and memory needed; and contourlet decomposition structure parameter can make significant effects on retrieval rates, especially scale number.
texture image retrieval dual tree complex contourlet transform Canberra distance standard deviation energy, skewness
Li-Wei Liu Xin-Wu Chen Zhi-Wei Ying
College of Physics and Electronic Engineering Xinyang Normal University Xinyane 464000, China College of Information Science and Engineering Henan University of Technology Zhengzhou 450001, Chin
国际会议
西安
英文
349-352
2011-05-27（万方平台首次上网日期，不代表论文的发表时间）