Texture Image Retrieval Based on Contourlet-2.3 and Generalized Gaussian Density Model

摘要：

In order to improve the retrieval rate of the original contourlet transform based texture image retrieval system, a contourlet-2.3 transform based texture image retrieval system was proposed. Generalized Gaussian Density (GGD) model parameters were cascaded to form feature vectors and Kullback-Leibler distance (KLD) function was used for similarity measure. Experimental results on 640 texture images from Vistex texture image database indicate that contourlet-2.3 transform based image retrieval system is superior to that of the original contourlet transform under the same system structure with almost same length of feature vectors, retrieval time and memory needed. Furthermore, GGD combined with KLD method has higher retrieval rates than energy based f eatures combined with Euclidean distance under comparable levels of computational complexity, decomposition parameters including the number of scale and directional subband on each scale selected in both contourlet transforms can make retrieval results quite different.

关键词： content-based image retrieval contourlet-2.3 transform contourlet transform texture image retrieval system generalized Gaussian density (GGD) model Kullback-Leibler distance retrieval rate

作者: Xinwu Chen Jianzhong Ma

作者单位: College of Physics and Electronics Xinyang Normal University Xinyang, China

会议类型: 国际会议

会议名称: The 2010 International Conference on Computer Application and System Modeling(2010计算机应用与系统建模国际会议 ICCASM 2010)

会议地点: 太原

会议语种:英文

页码: 199-203

在线出版日期: 2010-10-22（万方平台首次上网日期，不代表论文的发表时间）