Bundling centre for landmark image discovery

Abstract

This paper introduces a novel method to efficiently discover landmark images in large image collections. Each cluster is considered a combination of several sub-clusters, which are composed of images taken from different viewpoints of an identical landmark. For each sub-cluster, we find its local centre represented by a group of similar images and define it as the bundling centre (BC). Therefore, we start image discovery by identifying the BCs and accomplish the task by efficiently growing and merging those sub-clusters represented by different BCs. In our proposed method, we use a min-Hash-based method to build a sparse graph to avoid time-consuming, full-scale, exhaustive pairwise image matching. Based on the information provided by the sparse graph, BCs are identified as local dense neighbours sharing high intra-similarity. We have also proposed a weighted voting method to grow these BCs with high accuracy. More importantly, the fixed local centres ensure that each sub-cluster contains identical landmarks and generates results with high precision. In addition, compared to a single representative (iconic) image, the group of similar images obtained by each BC can provide more comprehensive cluster information and, thus, overcome the problem of low recall caused by information lost during visual word quantisation. We present the experimental results of three datasets and show that, without query expansion, our method can boost the landmark image discovery performances of current techniques.