Detecting near-duplicate images using segmented minhash algorithm

Abstract

The search of images using search engines in web results to a number of duplicate and near duplicate images with varying size and resolution. Near-duplicate (ND) image detection appears to be a significant issue in various applications such as copyright enforcement, news topic tracking, image and video search. This paper presents a method involving segmented minhash algorithm for indexing near-duplicate images. The method initially enhances the quality of query and web images and extracts the local invariant features by speeded up robust features (SURF). The segmented mishash algorithm then evaluates the similarity of the feature extracted images and locality sensitive hashing (LSH) performs indexing of near duplicate images in the web collections in respect of the query image. This paper also presents the results of a few sample images with a view of exhibiting the superiority of the proposed approach.