Belalia Amina, Belloulata Kamel, Redaoui Adil
High School of Computer Sciences, Sidi Bel Abbes 22000, Algeria.
RCAM Laboratory, Telecommunications Department, Sidi Bel Abbes University, Sidi Bel Abbes 22000, Algeria.
J Imaging. 2025 Jan 12;11(1):20. doi: 10.3390/jimaging11010020.
In recent years, deep-network-based hashing has gained prominence in image retrieval for its ability to generate compact and efficient binary representations. However, most existing methods predominantly focus on high-level semantic features extracted from the final layers of networks, often neglecting structural details that are crucial for capturing spatial relationships within images. Achieving a balance between preserving structural information and maximizing retrieval accuracy is the key to effective image hashing and retrieval. To address this challenge, we introduce Multiscale Deep Feature Fusion for Supervised Hashing (MDFF-SH), a novel approach that integrates multiscale feature fusion into the hashing process. The hallmark of MDFF-SH lies in its ability to combine low-level structural features with high-level semantic context, synthesizing robust and compact hash codes. By leveraging multiscale features from multiple convolutional layers, MDFF-SH ensures the preservation of fine-grained image details while maintaining global semantic integrity, achieving a harmonious balance that enhances retrieval precision and recall. Our approach demonstrated a superior performance on benchmark datasets, achieving significant gains in the Mean Average Precision (MAP) compared with the state-of-the-art methods: 9.5% on CIFAR-10, 5% on NUS-WIDE, and 11.5% on MS-COCO. These results highlight the effectiveness of MDFF-SH in bridging structural and semantic information, setting a new standard for high-precision image retrieval through multiscale feature fusion.
近年来，基于深度网络的哈希技术因其能够生成紧凑且高效的二进制表示而在图像检索中备受瞩目。然而，大多数现有方法主要关注从网络最后几层提取的高级语义特征，常常忽略对于捕捉图像内空间关系至关重要的结构细节。在保留结构信息和最大化检索精度之间取得平衡是有效图像哈希和检索的关键。为应对这一挑战，我们引入了用于监督哈希的多尺度深度特征融合（MDFF-SH），这是一种将多尺度特征融合集成到哈希过程中的新颖方法。MDFF-SH的特点在于其能够将低级结构特征与高级语义上下文相结合，合成强大且紧凑的哈希码。通过利用来自多个卷积层的多尺度特征，MDFF-SH确保在保持全局语义完整性的同时保留细粒度图像细节，实现一种和谐的平衡，从而提高检索精度和召回率。我们的方法在基准数据集上展现出卓越性能，与最先进的方法相比，在平均精度均值（MAP）上取得显著提升：在CIFAR-10上提升9.5%，在NUS-WIDE上提升5%，在MS-COCO上提升11.5%。这些结果凸显了MDFF-SH在弥合结构和语义信息方面的有效性，通过多尺度特征融合为高精度图像检索树立了新的标准。
