篇名 | Learning Complex Spatial Relation Model from Spatial Data |
---|---|
卷期 | 30:6 |
作者 | Sheng-Sheng Wang 、 Ru-Yi Dong |
頁次 | 123-136 |
關鍵字 | classification 、 complex spatial relations 、 feature selection 、 qualitative spatial reasoning 、 EI 、 MEDLINE 、 Scopus |
出刊日期 | 201912 |
DOI | 10.3966/199115992019123006010 |
Describing spatial relations is a challenging task in image understanding and contentbased image retrieval. There are only few works focused on describing complex spatial relations, and they usually use mathematical system which is not self-adapted, i.e. one model is only suitable for certain group of datasets. To address these problems, we proposed a self-adapted method for describing complex spatial relations. With our method, the complex spatial relation model can be quickly and accurately generated from very few labeled samples without prioriknowledge. The proposed method is tested on several benchmark datasets, and the experiment results demonstrate the superior performance and the robustness of our method.