Squirrel Search Optimization-based near-duplicate image detection

Abstract

ABSTRACT Near duplicate (ND) image detection is a significant issue in a modern online environment with a wide range of applications like the detection of copyright violations and saving of storage space. Several existing ND detection techniques are perhaps not suitable for online applications due to the large computational burden, and may not successfully detect NDs containing large smooth and plain regions. In addition, the K-means algorithm used in most of the existing methods yield sub-optimal quantization of visual words. This article employs a robust algorithm of Squirrel Search Optimization (SSO) for quantization, fast-hessian matrix-based detector (FHMBD) and FAST Corner Detector (FCD) for the detection of KPs at both plain and non-smooth regions of all images, SURF for computing descriptors and Principal Component Analysis (PCA) for dimensionality reduction. The results of the developed method presented on five image databases. The proposed method offered 99.9% accuracy, 98.67% sensitivity, and 99.91% specificity respectively.