An Enhanced Content-Based Image Retrieval with Similarity and ROI Analysis using AD-ES-CNN and EPL-Fuzzy

International Journal of Electrical and Electronics Engineering

© 2025 by SSRG - IJEEE Journal

Volume 12 Issue 12

Year of Publication : 2025

Authors : S. Ravi, Kamal Sutaria

10.14445/23488379/IJEEE-V12I12P105

How to Cite?

S. Ravi, Kamal Sutaria, "An Enhanced Content-Based Image Retrieval with Similarity and ROI Analysis using AD-ES-CNN and EPL-Fuzzy," SSRG International Journal of Electrical and Electronics Engineering, vol. 12,  no. 12, pp. 51-70, 2025. Crossref, https://doi.org/10.14445/23488379/IJEEE-V12I12P105

Abstract:

To retrieve relevant images from a large database, Content-Based Image Retrieval systems (CBIR) are designed centered on the query image’s visual content. Yet, the existing approaches often struggled with cluttered scenes, complex backgrounds, and the Region of Interest (ROI) in the image. Therefore, to address these challenges, this paper introduces You Only Live Once Version 3 (YOLO V3) and Renormalized Entropy - Gaussian Mixture Model (RE-GMM) approaches. Primarily, the image datasets are collected and pre-processed. The foreground and background objects are separated. Next, by using YOLOV3, the separated objects are detected. Further, by using Gram-Graph Cut (G2C), detected objects are segmented, and saliency mapping is carried out. Afterward, the features are extracted. Then, by using the Gaussian Mutation Tuna Swarm Optimization Algorithm (GM-TSOA), the features are selected from the extracted features. Afterward, by using the AD-ES-CNN algorithm, the object classification is performed. Also, the structural similarity index and semantic feature similarity are carried out by using the SIFT and Jaccard Index, respectively. Lastly, by using the EPL-Fuzzy approach, the object is retrieved and indexed. As per the experimental analysis, the proposed model attained 99.29% accuracy for the Caltech 256 image dataset and 98.5% accuracy for the Corel image dataset.

Keywords:

Region of Interest (ROI), T-splines Contrast Limited Adaptive Histogram Equalization (T-CLAHE), Renormalized Entropy - Gaussian Mixture Model (RE-GMM), Gram-Graph Cut (G2C), Gaussian Mutation Tuna Swarm Optimization Algorithm (GM-TSOA), Attention Drop E-Swish Convolutional Neural Networks (AD-ES-CNN), Exponential Piecewise Linear (EPL-Fuzzy).

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