Open Access

ARTICLE

Hybrid Evolutionary Algorithm Based Relevance Feedback Approach for Image Retrieval

Awais Mahmood^1,*, Muhammad Imran², Aun Irtaza³, Qammar Abbas⁴, Habib Dhahri^1,5, Esam Mohammed Asem Othman¹, Arif Jamal Malik⁶, Aaqif Afzaal Abbasi⁶

1 College of Applied Computer Science, King Saud University (Almuzahmiyah Campus), Riyadh, 11543, Saudi Arabia
2 Shaheed Zulfikar Ali Bhutto Institute of Science and Technology (Islamabad Campus), 44000, Pakistan
3 University of Engineering and Technology Taxila, Pakistan
4 International Islamic University, Islamabad, 44000, Pakistan
5 Faculty of Sciences and Technology, University of Kairouan, Sidi Bouzid, 4352, Tunisia
6 Department of Software Engineering, Foundation University Islamabad, Islamabad, 44000, Pakistan

* Corresponding Author: Awais Mahmood. Email:

(This article belongs to this Special Issue: Recent Advances in Deep Learning, Information Fusion, and Features Selection for Video Surveillance Application)

Computers, Materials & Continua 2022, 70(1), 963-979. https://doi.org/10.32604/cmc.2022.019291

Received 08 April 2021; Accepted 14 May 2021; Issue published 07 September 2021

Abstract

Searching images from the large image databases is one of the potential research areas of multimedia research. The most challenging task for nay CBIR system is to capture the high level semantic of user. The researchers of multimedia domain are trying to fix this issue with the help of Relevance Feedback (RF). However existing RF based approaches needs a number of iteration to fulfill user's requirements. This paper proposed a novel methodology to achieve better results in early iteration to reduce the user interaction with the system. In previous research work it is reported that SVM based RF approach generating better results for CBIR. Therefore, this paper focused on SVM based RF approach. To enhance the performance of SVM based RF approach this research work applied Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) before applying SVM on user feedback. The main objective of using these meta-heuristic was to increase the positive image sample size from SVM. Firstly steps PSO is applied by incorporating the user feedback and secondly GA is applied on the result generated through PSO, finally SVM is applied using the positive sample generated through GA. The proposed technique is named as Particle Swarm Optimization Genetic Algorithm- Support Vector Machine Relevance Feedback (PSO-G A-SVM-RF). Precisions, recall and F-score are used as performance metrics for the assessment and validation of PSO-GA-SVM-RF approach and experiments are conducted on coral image dataset having 10908 images. From experimental results it is proved that PSO-GA-SVM-RF approach outperformed then various well known CBIR approaches.

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