Indexing near-duplicate images in web search using minhash algorithm

Abstract

Near-duplicate images cause problems of redundancy and copyright infringement in large image collections. The trouble is minor in the web, where appropriation of images without acknowledgment of source is prevalent. Near duplicates can be exact copies or else differ slightly in their visual content. Near-duplicate detection has received substantial attention over the past few years due to the applications in copyright enforcement, organizing large size image databases, increasing focus on image search, redundancy elimination of logos, managing storage space by removing duplication, etc. In the paper, a method has been proposed for Indexing Near-Duplicate Images in the Web Search. First image enhancement is done in user query image then features are extracted based on SURF (Speeded up Robust Features) that is to extract the local invariant features of each image. After this similarity measure is calculated among the feature extracted images using min-hash algorithm. Finally, Locality Sensitive Hashing (LSH) is used for indexing near duplicate images based on user query. We demonstrate that our indexing approach is highly effective for collections of up to a few hundred thousand images.