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Although the bag-of-visual-words (BOVW) model in computer vision has been demonstrated successfully for the retrieval of particular objects, it suffers from limited accuracy when images of the same object are very different in terms of viewpoint or scale. Naively leveraging multiple views of the same object to query the database naturally alleviates this problem to some extent. However, the bottleneck appears to be the presence of background clutter, which causes significant confusion with images of different objects. To address this issue, we explore the structural organization of interest points within multiple query images and select those that derive from the tentative region of interest (ROI) to significantly reduce the negative contributions of confusing images. Specifically, we propose the use of a multi-layered undirected graph model built on sets of Hessian affine interest points to model the images’ elastic spatial topology. We detect repeating patterns that preserve a coherent local topology, show how these redundancies are leveraged to estimate tentative ROIs, and demonstrate how this novel interest point selection approach improves the quality of visual matching. The approach is discriminative in distinguishing clutter from interest points, and at the same time, is highly robust as regards variation in viewpoint and scale as well as errors in interest point detection and description. Large-scale datasets are used for extensive experimentation and discussion. Keywords Interest point selection Spatial topology Delaunay triangulation Multi-query image retrieval
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