Leveraging High-Resolution Features for Improved Deep Hashing-based Image Retrieval

arXiv:2403.13747v1 Announce Type: new
Abstract: 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 poses challenges 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 …

abstract alexnet architectures arxiv challenges cnns complexity convolutional neural networks cs.cv cs.ir datasets feature features hashing however image networks neural networks retrieval type vgg