An Effective Content Based Image Retrieval Using Multi Feature Fusion Algorithm with Optimized Retrieval Technique of Soft Computing Approach
Pushpalatha N
Faculty of Information Science and Technology, Multimedia University, Melaka 75450, Malaysia and Department of Electrical and Electronics Engineering, Sri Eshwar College of Engineering, Coimbatore, Tamil Nadu, India.
With the increasing digitization of healthcare, hospitals generate and store thousands of medical images daily, creating large-scale datasets that demand efficient retrieval solutions. Content-Based Image Retrieval (CBIR) systems address this by identifying relevant images based on visual features rather than textual metadata. While various CBIR approaches exist, many suffer from low precision, redundant retrievals, and slow query processing times. This paper introduces a novel hybrid CBIR framework that significantly improves retrieval accuracy and efficiency by integrating Principal Component Analysis (PCA) for texture extraction, Wavelet Transform (WT) for shape feature extraction, and Canonical Correlation Analysis (CCA) for advanced feature fusion. Unlike previous methods that rely on single-feature analysis or basic fusion strategies, our approach combines multiple complementary features into a unified representation, enhancing the system's ability to discern subtle patterns in medical images. CCA helps to find features from the medical images that are maximally related, e.g., the part of the breast that usually co-occur when someone is under observation. Additionally, we apply a customized classification strategy using Fuzzy Support Vector Machine optimized with Modified Whale Optimization Algorithm (FSVM-MWOA), which enhances model adaptability and retrieval precision. FSVM a variant of SVM that incorporates fuzzy logic to handle uncertainty and noisy data, MWOA an enhanced version of the bio-inspired Whale Optimization Algorithm, used here to optimize the parameters of the FSVM. Experimental results show that the proposed system achieves over 90% retrieval accuracy, reduces query response time by up to 40%, and minimizes redundancy, outperforming conventional CBIR techniques. This integrated approach not only addresses the limitations of existing methods but also introduces a scalable and robust solution tailored to the specific challenges of medical image datasets.
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CRediT Author Statement
The authors confirm contribution to the paper as follows:
Conceptualization: Pushpalatha N, Sumendra Yogarayan, Selvi A, Gunapriya D and Siti Fatimah Abdul Razak;
Methodology: Pushpalatha N, Sumendra Yogarayan and Selvi A;
Software: Gunapriya D and Siti Fatimah Abdul Razak;
Data Curation: Pushpalatha N, Sumendra Yogarayan, Selvi A, Gunapriya D and Siti Fatimah Abdul Razak;
Writing- Original Draft Preparation: Gunapriya D and Siti Fatimah Abdul Razak;
Visualization: Pushpalatha N, Sumendra Yogarayan and Selvi A;
Investigation: Gunapriya D and Siti Fatimah Abdul Razak;
Supervision: Gunapriya D and Siti Fatimah Abdul Razak;
Validation: Pushpalatha N, Sumendra Yogarayan and Selvi A;
Writing- Reviewing and Editing: Pushpalatha N, Sumendra Yogarayan, Selvi A, Gunapriya D and Siti Fatimah Abdul Razak;
All authors reviewed the results and approved the final version of the manuscript.
Acknowledgements
The author’s arc grateful to M. Kumarasamy College of Engineering, Sri Eshwar College of Engineering, Kebri Dehar University and Balls University for providing research environment to carry out the study.
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Sumendra Yogarayan
Faculty of Information Science and Technology, Multimedia University, Melaka 75450, Malaysia.
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Cite this article
Pushpalatha N, Sumendra Yogarayan, Selvi A, Gunapriya D and Siti Fatimah Abdul Razak, “An Effective Content Based Image Retrieval Using Multi Feature Fusion Algorithm with Optimized Retrieval Technique of Soft Computing Approach”, Journal of Machine and Computing, vol.5, no.4, pp. 1958-1970, October 2025, doi: 10.53759/7669/jmc202505153.
