A causality-enhanced multiresolution residual learning framework for image retrieval with fast osprey optimization.

The rapid expansion of large-scale image collections demands content-based image retrieval systems that are both semantically reliable and computationally efficient. Conventional CBIR approaches based on isolated visual descriptors often fail under high intra-class variation and inter-class ambiguity. This work proposes a causality-enhanced multiresolution residual learning framework that integrates deep representation learning, causal modeling, and lightweight optimization for robust image retrieval. A multiscale ResNet 50 backbone captures complementary fine-grained and high-level semantic features across multiple resolutions, while a causal variational autoencoder explicitly models latent spatial dependencies to improve semantic consistency and interpretability. Feature redundancy is minimized by an enhanced fast osprey optimization algorithm, enabling compact, discriminative feature selection at low computational cost. Extensive experiments on CIFAR-10, Oxford Flowers, and Corel 1000, using fivefold cross-validation, demonstrate consistent improvements over recently published thirteen state-of-the-art methods. Further gains in mean average precision, normalized discounted cumulative gain, and reduced retrieval time confirm the effectiveness, robustness, and practical viability of our approach across diverse retrieval scenarios. Moreover, the retrieval samples exhibit strong robustness against high intra-class variability and severe inter-class ambiguity, preserving discriminative consistency across visually similar categories.