Clustering near-duplicate images in large collections
Jun Jie Foo, Justin Zobel, Ranjan Sinha · 2007
Near-duplicate images introduce problems of redundancy and copyright infringement in large image collections. The problem is acute on the web, where appropriation of im-ages without acknowledgment of source is prevalent. In this paper, we present an effective clustering approach for near-duplicate images, using a combination of techniques from invariant image local descriptors and an adaptation of near-duplicate text-document clustering techniques; we extend our earlier approach of near-duplicate image pairwise iden-tification for this clustering approach. We demonstrate that our clustering approach is highly effective for collections of up to a few hundred thousand images. We also show — via experimentation with real examples — that our approach presents a viable solution for clustering near-duplicate im-ages on the Web.