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Shared Latent Characteristic Anchored Hash Codes Generation for Efficient Fine-Grained Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 13, 2026
Summary
This study introduces Characteristic Anchored Hash (CAH) codes for fine-grained image retrieval (FGIR). CAH improves retrieval accuracy and efficiency by directly addressing feature contradictions in hashing.
Area of Science:
- Computer Science
- Artificial Intelligence
- Machine Learning
Background:
- Hashing-based fine-grained image retrieval (FGIR) faces challenges balancing feature discriminability and compact binary code generation.
- Existing methods often degrade with shorter hash codes due to limitations in Hamming space representation.
Purpose of the Study:
- To propose a novel Characteristic Anchored Hash (CAH) codes generation method for improved FGIR.
- To address the inherent contradiction in feature-to-hash mapping for more effective retrieval.
Main Methods:
- Introduced learnable Characteristic-vectors (C-vectors) as anchors in feature space.
- Developed a characteristic matching loss and C-vector anchored feature refinement mechanism.
- Incorporated cross-layer semantic information transfer and object-constrained multi-region augmentation.
Main Results:
- The proposed CAH method significantly outperforms state-of-the-art techniques in retrieval accuracy.
- Achieved superior efficiency compared to existing FGIR methods.
- Demonstrated improved fine-grained feature learning without increased inference time computational overhead.
Conclusions:
- The CAH method offers a promising new direction for hashing-based FGIR.
- Effectively resolves the trade-off between feature discriminability and binary code compactness.