A novel Spatial-Scale Weighted GIST Descriptor for SAR Image Retrieval

摘要：

In this paper,a novel Spatial and Scale Weighted GIST(SSWGIST)descriptor is proposed for Synthetic Aperture Radar(SAR)image retrieval.Motivated by GIST features,images are represented by the mean values of adjacent and non-overlapped blocks of the Gabor filters response.Beyond that,our methods give those values different weights on different spatial and scale sites.The spatial weights are obtained adaptively by counting the ratio and significance of edges detected in the blocks.The scale weights obey the Gaussian distribution with special parameters toward given image datasets.Thus,the prominent identity of each block of filtering response can be reflected adaptively.A retrieval scheme experiment is carried on the Brodatz and SAR image datasets.The results reveal our algorithm’s efficient performances and superiorities.

关键词： SSWGIST traditional GIST Gabor filter banks Brodatz dataset SAR image dataset retrieval accuracy

作者: Pei Tao Chu He Chao Qian Hong Sun

作者单位: Signal Processing Lab.,Electronic Information School,Wuhan University,Wuhan 430079,P.R.China

会议类型: 国际会议

会议名称: 第一届智能网络与智能系统国际会议(ICINIS 2008)(The First International Conference on Intelligent Networks and Intelligent Systems)

会议地点: 武汉

会议语种:英文

在线出版日期: 2008-11-01（万方平台首次上网日期，不代表论文的发表时间）