The transformational and spatial proximities are important cues for identifying inliers from an appearance based match set because correct matches generally stay close in input images and share similar local transformations. However, most existing approaches only check one type of them or both types consecutively with manually set thresholds, and thus their matching accuracy and flexibility in handling large-scale images are limited. In this paper, we present an efficient clustering based approach to identify match inliers with both proximities simultaneously. It first projects the putative matches into a joint transformational-spatial space, where mismatches tend to scatter all around while correct matches gather together. A mode-seeking process based on joint kernel density estimation is then proposed to obtain significant clusters in the joint space, where each cluster contains matches mapping the same object across images with high accuracy. Moreover, kernel bandwidths for measuring match proximities are adaptively set during density estimation, which enhances its applicability for matching different images. Experiments on three standard datasets show that the proposed approach delivers superior performance on a variety of feature matching tasks, including multi-object matching, duplicate object matching and object retrieval.

变换和空间近似是从基于外观的匹配集中识别内点的重要线索，因为正确的匹配通常在输入图像中保持接近并共享相似的局部变换。然而，大多数现有方法仅通过手动设置阈值来检查其中一种或两种类型，因此它们在处理大规模图像时的匹配精度和灵活性受到限制。在本文中，我们提出了一种有效的基于聚类的方法来同时识别具有两个近似值的匹配内点。它首先将假定的匹配项投射到一个联合的转换空间空间中，在该空间中，不匹配趋于分散，而正确匹配则聚集在一起。然后提出了一种基于联合核密度估计的模式寻找过程，以获得联合空间中的重要集群，其中每个集群包含以高精度映射同一对象跨图像的匹配项。此外，在密度估计过程中自适应地设置用于测量匹配接近度的内核带宽，这增强了其匹配不同图像的适用性。在三个标准数据集上的实验表明，所提出的方法在各种特征匹配任务上具有卓越的性能，包括多对象匹配、重复对象匹配和对象检索。这增强了其匹配不同图像的适用性。在三个标准数据集上的实验表明，所提出的方法在各种特征匹配任务上具有卓越的性能，包括多对象匹配、重复对象匹配和对象检索。这增强了其匹配不同图像的适用性。在三个标准数据集上的实验表明，所提出的方法在各种特征匹配任务上具有卓越的性能，包括多对象匹配、重复对象匹配和对象检索。