Title: Detecting near-duplicate images using segmented minhash algorithm

Authors: S. Thaiyalnayaki; J. Sasikala; R. Ponraj

Addresses: Department of Computer Science and Engineering, Dhanalakshmi Srinivasan College of Engineering and Technology, ECR, Mamallapuram-603104, Chennai, India ' Department of Information Technology, Annamalai University, Annamalainagar-608002, Tamil Nadu, India ' Dhanalakshmi Srinivasan College of Engineering and Technology, ECR Mamallapuram-603104, Chennai, India

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.

Keywords: indexing; near-duplicates; near-duplicate detection; image enhancement.

DOI: 10.1504/IJAIP.2019.096963

International Journal of Advanced Intelligence Paradigms, 2019 Vol.12 No.1/2, pp.192 - 206

Received: 14 Nov 2016
Accepted: 20 Dec 2016
Published online: 14 Dec 2018 *

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