NOVEL CBIR System Using Spark MAP-Reduce with a Firefly Macqueen’s K-Means Clustering Algorithm

Abstract

Nowadays content-based image retrieval (CBIR) has been an active research area, demonstrating a feasible solution for recovering similar images from an image mine. The amount of digital images increases exponentially day by. Storage requirements for these images may increase from Gigabytes to Petabytes. Searching and retrieving the relevant images from such a large volume of image datasets, based on their contents, play a dynamic role in various applications of computer vision. The time taken to retrieve the images is more, and the accuracy of retrieved images is less in the existing systems. The limitation of Hadoop map-reduce is the lack of performing real-time tasks efficiently. A proficient content-based image retrieval framework based on Spark Map-Reduce with a Firefly MacQueen’s k-means clustering (FMKC) algorithm and Bag of visual word (BoVW) is proposed to achieve high accuracy for big data. The Apache spark programming can be used to productively recover pictures with less retrieval time and retrieve the accurate images from the big database that resembles the query image. The experimental results demonstrate that the method proposed in our work outperforms the state-of-the-art methods in terms of accuracy of the retrieved images and average retrieval time. The proposed system is 93% accurate, and it is easier to retrieve the images from the large database.

Keywords:

• SURF feature extraction
• BoVW classification
• Firefly Macqueen’s k-means clustering (FMKC)
• Content-Based Image Retrieval (CBIR)
• Apache Spark
• Contrast Limited Adaptive Histogram Equalization with Unsharp masking technique (CLAHEUMT)

ACKNOWLEDGEMENTS

The authors thank the management of Arunachala College of Engineering for Women and the anonymous reviewers for the help in strengthening this paper.

Disclosure statement

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

Additional information

Notes on contributors

T. Sunitha

T Sunitha is working as assistant professor in Arunachala College of Engineering for Women. She received her bachelor's degree in computer science & engineering from Manonmanium Sundaranar University and master's degree in computer science & engineering from Anna University, Chennai. She is currently pursuing her PhD degree in Arunachala College of Engineering for Women, Vellichanthai at Anna University, Chennai, India. She has published a few conference papers and participated in some international conferences. Her research interests include image processing, bigdata, and machine learning.

T. S. Sivarani

T S Sivarani ME, PhD is working as professor and head in the Department of Electrical and Electronics Engineering in Arunachala College of Engineering for Women, Vellichanthai, Nagercoil at Anna University Chennai. She received her BE degree in electrical and electronics engineering from Manonmanium Sundaranar University and ME degree in power systems engineering from Government College of Technology, Coimbatore. She has published 8 research papers in international journals, 7 papers in international conferences, and three papers in national conferences. Her research area includes power electronics, AC drives and control, random PWM, matrix converters, and machine learning and artificial intelligence. Email: [email protected]