AI-driven feature descriptor design using CNNs and transfer learning for enhanced image retrieval accuracy and efficiency

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Image retrieval systems rely on robust and discriminative feature descriptors for effective Content-Based Image Retrieval (CBIR). Traditional handcrafted descriptors, such as SIFT and SURF, although computationally efficient, exhibit limitations in handling large-scale datasets, domain variability, and semantic representation. To address these challenges, this paper proposes an AI-driven feature descriptor framework based on Convolutional Neural Networks (CNNs) and transfer learning. The proposed approach leverages pre-trained architectures, including ResNet and VGG, and integrates dimensionality reduction using Principal Component Analysis (PCA) along with attention-based pooling to generate compact and semantically rich feature representations. The framework is evaluated on benchmark image retrieval datasets, including Oxford5k, Paris6k, and Holidays, under standardized experimental settings. Quantitative results demonstrate that the proposed method achieves a mean Average Precision (mAP) of 85.9 %, outperforming traditional handcrafted descriptors and baseline CNN features. Furthermore, High-dimensional CNN features (e.g., 2048-d) are compressed to 128 dimensions using PCA, resulting in significant improvements in storage efficiency and retrieval speed. Overall, the proposed method provides an effective balance between retrieval accuracy, computational efficiency, and adaptability across diverse image domains, making it suitable for large-scale real-world CBIR applications.