Effective scene matching with local feature representatives

Scene matching measures the similarity of scenes in photos and is of central importance in applications where we have to properly organize large amount of digital photos by scene categories. In this paper, we present a novel scene matching method using local features representatives. For a given image, its scene is compactly represented as a set of cluster centers, called local feature representatives, where the clusters are obtained using the affinity propagation (AP) algorithm to aggregate local features according to their spatial closeness and appearance similarity. The similarity of scenes in two images is then measured by a modified Earth Mover Distance (EMD) between their corresponding sets of local feature representatives. Empirical experiments on real world photos shows that our method is comparable to the state-of-the-arts.