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Efficient Rank-Based Diffusion Process with Assured Convergence

Department of Statistics, Applied Mathematics and Computing (DEMAC), São Paulo State University (UNESP), Rio Claro 13506-900, Brazil
Department of Computer and Information Sciences, Temple University, Philadelphia, PA 19122-1801, USA
Author to whom correspondence should be addressed.
Academic Editor: Gonzalo Pajares Martinsanz
Received: 26 January 2021 / Revised: 27 February 2021 / Accepted: 1 March 2021 / Published: 8 March 2021
(This article belongs to the Special Issue 2020 Selected Papers from Journal of Imaging Editorial Board Members)
Visual features and representation learning strategies experienced huge advances in the previous decade, mainly supported by deep learning approaches. However, retrieval tasks are still performed mainly based on traditional pairwise dissimilarity measures, while the learned representations lie on high dimensional manifolds. With the aim of going beyond pairwise analysis, post-processing methods have been proposed to replace pairwise measures by globally defined measures, capable of analyzing collections in terms of the underlying data manifold. The most representative approaches are diffusion and ranked-based methods. While the diffusion approaches can be computationally expensive, the rank-based methods lack theoretical background. In this paper, we propose an efficient Rank-based Diffusion Process which combines both approaches and avoids the drawbacks of each one. The obtained method is capable of efficiently approximating a diffusion process by exploiting rank-based information, while assuring its convergence. The algorithm exhibits very low asymptotic complexity and can be computed regionally, being suitable to outside of dataset queries. An experimental evaluation conducted for image retrieval and person re-ID tasks on diverse datasets demonstrates the effectiveness of the proposed approach with results comparable to the state-of-the-art. View Full-Text
Keywords: diffusion; rank; image retrieval; convergence diffusion; rank; image retrieval; convergence
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MDPI and ACS Style

Guimarães Pedronette, D.C.; Pascotti Valem, L.; Latecki, L.J. Efficient Rank-Based Diffusion Process with Assured Convergence. J. Imaging 2021, 7, 49.

AMA Style

Guimarães Pedronette DC, Pascotti Valem L, Latecki LJ. Efficient Rank-Based Diffusion Process with Assured Convergence. Journal of Imaging. 2021; 7(3):49.

Chicago/Turabian Style

Guimarães Pedronette, Daniel C.; Pascotti Valem, Lucas; Latecki, Longin J. 2021. "Efficient Rank-Based Diffusion Process with Assured Convergence" J. Imaging 7, no. 3: 49.

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