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Topological Spatial Verification for Instance Search

机译：实例搜索的拓扑空间验证

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This paper proposes an elastic spatial verification method for Instance Search, particularly for dealing with non-planar and non-rigid queries exhibiting complex spatial transformations. Different from existing models that map keypoints between images based on a linear transformation (e.g., affine, homography), our model exploits the topological arrangement of keypoints to address the non-linear spatial transformations that are extremely common in real life situations. In particular, we propose a novel technique to elastically verify the topological spatial consistency with the triangulated graph through a “sketch-and-match” scheme. The spatial topology configuration, emphasizing relative positioning rather than absolute coordinates, is first sketched by a triangulated graph, whose edges essentially capture the topological layout of the corresponding keypoints. Next, the spatial consistency is efficiently estimated as the number of common edges between the triangulated graphs. Compared to the existing methods, our technique is much more effective in modeling the complex spatial transformations of non-planar and non-rigid instances, while being compatible to instances with simple linear transformations. Moreover, our method is by nature more robust in spatial verification by considering the locations, rather than the local geometry of keypoints, which are sensitive to motions and viewpoint changes. We evaluate our method extensively on three years of TRECVID datasets, as well as our own dataset MQA, showing large improvement over other methods for the task of Instance Search.
机译：本文提出了一种用于实例搜索的弹性空间验证方法，特别是用于处理表现出复杂空间变换的非平面和非刚性查询。与基于线性变换（例如仿射，单应性）在图像之间映射关键点的现有模型不同，我们的模型利用关键点的拓扑结构来解决在现实生活中极为常见的非线性空间变换。特别是，我们提出了一种新颖的技术，可以通过“草图匹配”方案弹性地验证与三角图的拓扑空间一致性。首先通过三角图绘制强调相对位置而不是绝对坐标的空间拓扑配置，该图的边缘基本上捕获了相应关键点的拓扑布局。接下来，将空间一致性有效地估计为三角图之间公共边的数量。与现有方法相比，我们的技术在建模非平面和非刚性实例的复杂空间变换方面更为有效，同时与具有简单线性变换的实例兼容。此外，通过考虑位置而不是关键点的局部几何形状（对运动和视点变化敏感），我们的方法本质上在空间验证方面更强大。我们对三年的TRECVID数据集以及我们自己的数据集MQA进行了广泛的评估，显示出与其他方法相比，实例搜索任务有了很大的改进。

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