| 图像匹配的物体空间关系推理表达 |
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| 引用本文： | 李钦,游雄,李科,王玮琦.图像匹配的物体空间关系推理表达[J].测绘学报,2021,50(1):117-131. |
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| 作者姓名： | 李钦 游雄 李科 王玮琦 |
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| 作者单位： | 信息工程大学地理空间信息学院,河南郑州450001;信息工程大学地理空间信息学院,河南郑州450001;信息工程大学地理空间信息学院,河南郑州450001;信息工程大学地理空间信息学院,河南郑州450001 |
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| 基金项目： | 国家自然科学基金（41871322） |
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| 摘 要： | 物体空间关系指的是物体在欧氏空间中的邻近关系,根据图像中包含物体的邻近关系解决图像匹配的问题。本文首先基于对比机制训练物体块特征提取网络,构建物体块深度特征,该特征可以有效匹配不同图像中的相同物体块;其次,基于已有的先验图像数据推理表达图像中物体的空间邻近关系,构建场景物体空间邻近图;进而基于该空间邻近图计算场景图像对的空间邻近度,完成图像空间关系匹配。试验表明不匹配图像间的空间邻近度一般为0,而匹配图像间的空间邻近度一般大于0,本文空间关系匹配涉及多个物体间的相互关系,具有更强的稳健性,其匹配效果明显优于对比试验中的其他方法,可以高效稳定地完成图像匹配任务。
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| 关 键 词： | 深度特征 空间邻近图 空间关系匹配 物体块所属节点 |
| 收稿时间： | 2019-12-09 |
| 修稿时间： | 2020-09-14 |
Spatial relation reasoning and representation for image matching |
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| LI Qin,YOU Xiong,LI Ke,WANG Weiqi.Spatial relation reasoning and representation for image matching[J].Acta Geodaetica et Cartographica Sinica,2021,50(1):117-131. |
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| Authors: | LI Qin YOU Xiong LI Ke WANG Weiqi |
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| Institution: | Institute of Geospatial Information, Information Engineering University, Zhengzhou 450001, China |
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| Abstract: | The spatial relationship in the paper means the spatial adjacency relation between the objects in word space, and the paper proposes a novel image matching method based on analyzing the spatial adjacency relation between the object contained in the image pair. In the method, the feature extraction network is first trained based on comparison mechanism, and the well-trained model could produce the deep features for the objects, which could effectively match the consistent objects across images. Then the priori images are employed to deduce the spatial adjacency information between different objects, which is further represented by the spatial adjacency graph. Finally, the spatial relation matching between the image pair is conducted by calculating the spatial adjacency score based on the spatial adjacency graph. The experimental results demonstrate that the spatial adjacency scores of the non-matched image pairs generally equal to 0, and those of the matched pairs are generally greater than 0. As several objects are involved, the proposed spatial relation matching method could achieve high robustness, it outperforms other methods in the comparison experiments, and it could effectively complete the image matching task with high efficiency. |
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| Keywords: | deep feature spatial adjacency graph image matching corresponding node of object |
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