Hash Code Generation using Deep Feature Selection Guided Siamese Network for Content-Based Medical Image Retrieval

Öztürk, Şaban

doi:10.35378/gujs.710730

Cited by 16 publications

(1 citation statement)

References 41 publications

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“…The emergence of deep hash learning methods is inspired by the strong feature learning capabilities of DNNs. It has a wide range of applications in various fields, such as medical image retrieval [39][40][41]. In the RSI field, Liu et al [20] proposed a supervised deep hash retrieval model leveraging generative adversarial networks (GAN), termed GAN-assisted hashing.…”

Section: Deep Hash Learning Methodsmentioning

confidence: 99%

DMCH: A Deep Metric and Category-Level Semantic Hashing Network for Retrieval in Remote Sensing

Huang,

Cheng,

Shao

et al. 2023

Remote Sensing

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The effectiveness of hashing methods in big data retrieval has been proved due to their merit in computational and storage efficiency. Recently, encouraged by the strong discriminant capability of deep learning in image representation, various deep hashing methodologies have emerged to enhance retrieval performance. However, maintaining the semantic richness inherent in remote sensing images (RSIs), characterized by their scene intricacy and category diversity, remains a significant challenge. In response to this challenge, we propose a novel two-stage deep metric and category-level semantic hashing network termed DMCH. First, it introduces a novel triple-selection strategy during the semantic metric learning process to optimize the utilization of triple-label information. Moreover, it inserts a hidden layer to enhance the latent correlation between similar hash codes via a designed category-level classification loss. In addition, it employs additional constraints to keep bit-uncorrelation and bit-balance of generated hash codes. Furthermore, a progressive coarse-to-fine hash code sorting scheme is used for superior fine-grained retrieval and more effective hash function learning. Experiment results on three datasets illustrate the effectiveness and superiority of the proposed method.

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