Clustering Near-Duplicate Images in Large Collections

Near-duplicate images introduce problems of redundancy and copyright infringement in large image collections. The problem is acute on the web, where appropriation of images without acknowledgment of source is prevalent. In this paper, we present an effective clustering approach for nearduplicate images, using a combination of techniques from invariant image local descriptors and an adaptation of nearduplicate text-document clustering techniques; we extend our earlier approach of near-duplicate image pairwise identification 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 images on the Web