TY - GEN
T1 - Leveraging High-Resolution Features for Improved Deep Hashing-Based Image Retrieval
AU - Berriche, Aymene
AU - Adjal, Mehdi Zakaria
AU - Baghdadi, Riyadh
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
PY - 2025
Y1 - 2025
N2 - Deep hashing techniques have emerged as the predominant approach for efficient image retrieval. Traditionally, these methods utilize pre-trained convolutional neural networks (CNNs) such as AlexNet and VGG-16 as feature extractors. However, the increasing complexity of datasets reveals the limitations for these backbone architectures in capturing meaningful features essential for effective image retrieval. In this study, we explore the efficacy of employing high-resolution features learned through state-of-the-art techniques for image retrieval tasks. Specifically, we propose a novel methodology that utilizes High-Resolution Networks (HRNets) as the backbone for the deep hashing task, termed High-Resolution Hashing Network (HHNet). Our approach demonstrates superior performance compared to existing methods across all tested benchmark datasets, including CIFAR-10, NUS-WIDE, MS COCO, and ImageNet. This performance improvement is more pronounced for complex datasets, which highlights the need to learn high-resolution features for intricate image retrieval tasks. Furthermore, we conduct a comprehensive analysis of different HRNet configurations and provide insights into the optimal architecture for the deep hashing task.
AB - Deep hashing techniques have emerged as the predominant approach for efficient image retrieval. Traditionally, these methods utilize pre-trained convolutional neural networks (CNNs) such as AlexNet and VGG-16 as feature extractors. However, the increasing complexity of datasets reveals the limitations for these backbone architectures in capturing meaningful features essential for effective image retrieval. In this study, we explore the efficacy of employing high-resolution features learned through state-of-the-art techniques for image retrieval tasks. Specifically, we propose a novel methodology that utilizes High-Resolution Networks (HRNets) as the backbone for the deep hashing task, termed High-Resolution Hashing Network (HHNet). Our approach demonstrates superior performance compared to existing methods across all tested benchmark datasets, including CIFAR-10, NUS-WIDE, MS COCO, and ImageNet. This performance improvement is more pronounced for complex datasets, which highlights the need to learn high-resolution features for intricate image retrieval tasks. Furthermore, we conduct a comprehensive analysis of different HRNet configurations and provide insights into the optimal architecture for the deep hashing task.
KW - Deep Hashing
KW - High-Resolution networks
KW - Image Retrieval
UR - http://www.scopus.com/inward/record.url?scp=105006591844&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=105006591844&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-88711-6_28
DO - 10.1007/978-3-031-88711-6_28
M3 - Conference contribution
AN - SCOPUS:105006591844
SN - 9783031887109
T3 - Lecture Notes in Computer Science
SP - 440
EP - 453
BT - Advances in Information Retrieval - 47th European Conference on Information Retrieval, ECIR 2025, Proceedings
A2 - Hauff, Claudia
A2 - Macdonald, Craig
A2 - Jannach, Dietmar
A2 - Kazai, Gabriella
A2 - Nardini, Franco Maria
A2 - Pinelli, Fabio
A2 - Silvestri, Fabrizio
A2 - Tonellotto, Nicola
PB - Springer Science and Business Media Deutschland GmbH
T2 - 47th European Conference on Information Retrieval, ECIR 2025
Y2 - 6 April 2025 through 10 April 2025
ER -