Stacked and decorrelated hashing with AdapTanh for large-scale fine-grained image retrieval

Abstract

Large-scale fine-grained image retrieval is a challenging task in computer vision, often addressed through learning to hash. Current methods typically use deep neural networks to create compact hash functions, but feature extraction through fusion or cascading can introduce coupling, limiting model generalization. To overcome this, we propose a multi-model stacked and decorrelated hashing approach, utilizing parallel backbone networks as feature extractors. A decorrelation objective, based on diagonal matrices, minimizes feature correlation, ensuring diverse hashing features. We also introduce a relaxation strategy to enhance the sensitivity of the output layer to fine-grained features. Experiments on various datasets demonstrate our model’s superior retrieval performance over state-of-the-art deep hashing methods.