CIRCULAR RELEVANCE FEEDBACK FOR REMOTE SENSING IMAGE RETRIEVAL

被引：0

作者：

Tang, Xu ^{[1
]}

Zhang, Xiangrong ^{[1
]}

Liu, Fang ^{[1
]}

Jiao, Licheng ^{[1
]}

机构：

[1] Xidian Univ, Sch Artificial Intelligence,Key Lab Intelligent P, Int Res Ctr Intelligent Percept & Computat,Minist, Joint Int Res Lab Intelligent Percept & Computat, Xian, Shaanxi 710071, Peoples R China

来源：

IGARSS 2018 - 2018 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM | 2018年

基金：

中国博士后科学基金;

关键词：

Relevance feedback; remote sensing image retrieval; CLASSIFICATION;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Relevance feedback (RF) is a popular reranking technique, which aims at improving the performance of image retrieval by taking the user's opinions into account. In this paper, we introduce a new RF method, named circular relevance feedback (CRF), to enhance the behavior of remote sensing image retrieval (RSIR). Instead of the manual selection used in the common RF method, we adopt the active learning (AL) algorithm to select the samples from the initial results automatically in each RF iteration. Moreover, to ensure the selected images are representative and informative enough, we choose different AL algorithms to complete the different RF processes. Finally, the contributions of all AL-driven RF methods are integrated using a circular fusion scheme. The encouraging experimental results on the ground truth RS image archive illustrate that our CRF is useful for enhancing the performance of RSIR. In addition, compared with many existing RF methods, our CRF achieves improved behavior.

引用

页码：8953 / 8956

页数：4

共 13 条

[11] Fusion Similarity-Based Reranking for SAR Image Retrieval [J].