Seagull optimization-based near-duplicate image detection in large image databases

ABSTRACT

Many of the near-duplicate (ND) image detection methods involve a greater number of interest points (IPs) and large dimensions of the feature descriptors requiring huge computations and are unsuitable for large image databases. They may fail to detect NDs if the query and images in the database contain sparse IPs due to low entropy. Besides, the k-means algorithm used for the quantization of visual words may land at a sub-optimal minimum for descriptors because of their distance distribution in feature space. This article presents a new ND image detection method, which uniformly distributes the IPs over low and high entropy regions, reduces the dimension of feature descriptors using discrete wavelet transform (DWT) and employs Seagull Optimization algorithm (SOA) for optimally forming the visual words. It examines proposed method performs on image databases of various sizes and shows that the developed method is more reliable and computationally efficient than the alternatives.

KEYWORDS:

• Near-duplicate images
• seagull optimization
• K-means clustering
• scale invariant features transform
• bag of visual words
• discrete wavelet transform
• visual words
• computationally efficient

Acknowledgements

The authors thankfully acknowledge the administrative officers of Annamalai University for the computing and internet facilities provided to perform this work.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Data sharing does not apply to this article as no new data has been created or analysed in this study.

Additional information

Notes on contributors

Srinidhi Sundaram

Srinidhi Sundaram, Assistant Professor, Department Computer Science and Engineering, Agni College of Technology, Chennai, Tamil Nadu, received the B.Sc Degree in Computer Science from Pandit Ravishankar Shukla University, M.C.A from Madurai Kamaraj University and M. Tech Degree in Information Technology from Sathyabama University, in 1999, 2003 and 2010 respectively. She is presently pursuing part-time Ph.D, Department of Information Technology, Annamalai University, Tamil Nadu, India. She has 15 years of teaching experience and is specialized in the area of image forensics, soft computing and metaheuristic optimization.

S. Kamalakkannan

S. Kamalakkannan received his Ph.D. Degree in Computer Science from Vels Institute of Science, Technology & Advanced Studies (VISTAS), Chennai, Tamil Nadu, India. He is currently working as Associate Professor, Department of Information Technology, School of Computing Sciences, Vels Institute of Science, Technology & Advanced Studies (VISTAS), Chennai, Tamil Nadu, India, which is a well-known university. He has 18 years of teaching experience in both UG and PG level. His research interest includes Data Mining, Big Data Analytics, Cloud Computing and Block Chain Technology. He has produced one Ph.D Research scholar. He has published more than 50 research articles in various International journals such as Sci, WOS, Scopus and UGC referred journals. He serves as an Examiner in various Universities and Colleges. He received Best Young Scientist award and Best Faculty award.

Sasikala Jayaraman

Sasikala Jayaraman received the B.E. Degree in Electronics and Communication Engineering from Madras University, India in 1993, and the M.E and Ph. D degrees in Computer Science and Engineering from Annamalai University in 2005 and 2011 respectively. She has been working as an Associate Professor, Department of Information Technology, Annamalai University, Tamil Nadu, India since 1999. Her research interests are in the area of optimization, evolutionary algorithms and image processing.