Content-based image retrieval by classification with reinforcement optimisation evolutionary machine learning with applications

ABSTRACT

Content Based Image Retrieval (CBIR) plays a significant role in identifying the similarity of images with large datasets. It is identified based on the size, colour, and texture features of the image. But in such conditions, it is complex to determine the features of query images in large datasets and does not show accurate similarity when compared with every image in the retrieval process. In order to perform an efficient similarity of images, a novel Machine Learning (ML) approach Kernelized Radial Basis Auto-Encoder Function Neural Network (Ker_RadBAEFNN) technique is proposed that performs the individual image classification in the retrieval process. Moreover, the neural networks are optimised based on the reinforcement process and perform the extraction process regarding individual images. Further,reinforcement-based optimisation estimates the images in neural networks for undertaking an automatic feature extraction of query images. The performance of the classification process is validated based on MNIST, METU, and COCO datasets that determined the efficiency of the recognition and classification process of image retrieval. The experimental analysis is carried out based on various measures such as accuracy, precision, recall, F1-score, RMSE, and MAPE for the proposed and existing GLCM-ABC, PSO-ANN, IRB-CNN, FAGWO, and OCAM methods. The analysis shows that the performance of the proposed attained better effectiveness with attained accuracy by 98% and diminished for state-of-the-art techniques as 92%, 95%, 94%, 96.8%, as well as 96%, respectively. Compared to existing methods, the accuracy rate of the proposed method is maximised by 1.3%.

KEYWORDS:

• CBIR
• DL
• evolutionary machine learning
• classification
• optimisation

Acknowledgements

The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University, Abha, Kingdom of Saudi Arabia for funding this work through Large Groups RGP.2/19/1445.

Disclosure statement

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

Authors’ contributions

All authors agreed on the content of the study. ASCB, LS, SQ, SU, LSPA and SS collected all the data for analysis. ASCB agreed on the methodology. ASCB, LS, SQ, SU, LSPA and SS completed the analysis based on agreed steps. Results and conclusions are discussed and written together. The author read and approved the final manuscript.

Informed consent

Informed consent was obtained from all individual participants included in the study.