First Index-Free Manifold Ranking-Based Image Retrieval with Output Bound

Lin, Dandan; Wei, Victor Junqiu; Wong, Raymond Chi-Wing

doi:10.1109/icdm.2019.00150

Cited by 4 publications

(1 citation statement)

References 24 publications

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“…Then, [8] proposed to use manifold ranking to enhance the relationship among images in context. On the basis of it, many improved methods [9]- [13] have been proposed and reached higher performance.…”

Section: Introductionmentioning

confidence: 99%

Graph Regularized Hierarchical Diffusion Process With Relevance Feedback for Medical Image Retrieval

Yao

Zhong

et al. 2021

IEEE Access

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Befitting from the interpretability and the capacity in capturing the underlying manifold structure, diffusion process (DP) has attracted increasing attention in the field of image retrieval. Within it, hierarchical diffusion process (HDP) has achieved satisfactory results in retrieved performance and complexity. However, the existing hierarchical diffusion process methods only diffuse the affinity values in low-level visual space without considering the high-level semantic information, which cause the problem of semantic gap. To overcome these problems, we propose a Graph Regularized Hierarchical Diffusion Process (GRHDP) method with relevance feedback, and apply it to retrieve medical images. The proposed algorithm firstly establishes a hierarchical structure of the images in medical image database and spreads the affinity values among query images and top-layer images by graph regularization diffusion. Then relevance feedback is introduced to adjust the similarity between query images and retrieved images in top layer, and the affinity values are diffused again according to labeled information of feedback. Finally, the similarity between queries and others in database can be obtained by interpolating the diffused results on the top layer from top to bottom. The experimental results show that our proposed GRHDP with relevance feedback has achieved better retrieval performance than manifold ranking and regularized diffusion process (RDP) when returning top retrieved images.

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Section: Introductionmentioning

confidence: 99%