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<p>Recently, image retrieval has become an emerging field, and it plays a versatile role in industries such as marketing and design, health care, security, and entertainment. Traditional image retrieval systems have challenges with high-dimensional feature spaces, which lead to inefficiency in processing and retrieval times. Moreover, as image volume increases, maintaining performance and speed becomes increasingly challenging, particularly in real-time applications. Hence, the proposed study presents an advanced Content-Based Image Retrieval (CBIR) system that integrates Quantum Convolutional Neural Networks (QCNN) with Deep Reinforcement Learning (DRL) to enhance retrieval accuracy and adaptability, addressing challenges posed by high-dimensional feature spaces in traditional systems. The framework utilises quantum-inspired feature extraction alongside classical CNNs to effectively capture complex image representations. Experiments performed on diverse datasets (Caltech-101, CIFAR-10, FTVL) demonstrate that the proposed system achieves exceptional mean Average Precision (mAP) scores: 1.0 for 9 out of 10 classes in CIFAR-10 (with 0.99 for the remaining class), 0.972 on FTVL, and 0.905 on Caltech-101, significantly outperforming traditional models such as FDenseNet (0.958 on FTVL and 0.891 on Caltech-101). In comparative analysis across different bit lengths, the proposed model achieves mAP scores of 0.9754 (16-bit), 0.9858 (32-bit), 0.9931 (48-bit), and 0.9986 (64-bit), surpassing state-of-the-art methods. Ablation studies confirm that the combination of Quantum CNN with Synergistic Policy Improvement Meta-Optimization (SPIMO) and dynamic feature weighting attains the highest mAP of 1.0, validating the effectiveness of the proposed approach. The system employs cosine similarity for image comparison, and the incorporation of feedback mechanisms facilitates continuous learning, enhancing robustness in real-world applications. To support reproducibility, the source code and implementation details will be made publicly available upon acceptance of the manuscript and are available from the corresponding author upon reasonable request.</p>

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Effective content-based image retrieval in deep reinforcement learning and quantum approach

  • 期刊论文

  • 发表日期：

    2026年7月3日

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  • Mohemmed Sha,
  • Adel Binbusayyis

摘要

Recently, image retrieval has become an emerging field, and it plays a versatile role in industries such as marketing and design, health care, security, and entertainment. Traditional image retrieval systems have challenges with high-dimensional feature spaces, which lead to inefficiency in processing and retrieval times. Moreover, as image volume increases, maintaining performance and speed becomes increasingly challenging, particularly in real-time applications. Hence, the proposed study presents an advanced Content-Based Image Retrieval (CBIR) system that integrates Quantum Convolutional Neural Networks (QCNN) with Deep Reinforcement Learning (DRL) to enhance retrieval accuracy and adaptability, addressing challenges posed by high-dimensional feature spaces in traditional systems. The framework utilises quantum-inspired feature extraction alongside classical CNNs to effectively capture complex image representations. Experiments performed on diverse datasets (Caltech-101, CIFAR-10, FTVL) demonstrate that the proposed system achieves exceptional mean Average Precision (mAP) scores: 1.0 for 9 out of 10 classes in CIFAR-10 (with 0.99 for the remaining class), 0.972 on FTVL, and 0.905 on Caltech-101, significantly outperforming traditional models such as FDenseNet (0.958 on FTVL and 0.891 on Caltech-101). In comparative analysis across different bit lengths, the proposed model achieves mAP scores of 0.9754 (16-bit), 0.9858 (32-bit), 0.9931 (48-bit), and 0.9986 (64-bit), surpassing state-of-the-art methods. Ablation studies confirm that the combination of Quantum CNN with Synergistic Policy Improvement Meta-Optimization (SPIMO) and dynamic feature weighting attains the highest mAP of 1.0, validating the effectiveness of the proposed approach. The system employs cosine similarity for image comparison, and the incorporation of feedback mechanisms facilitates continuous learning, enhancing robustness in real-world applications. To support reproducibility, the source code and implementation details will be made publicly available upon acceptance of the manuscript and are available from the corresponding author upon reasonable request.