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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Efficient Rank-Based Diffusion Process with Assured Convergence

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Guimaraes Pedronette, Daniel Carlos [1] ; Pascotti Valem, Lucas [1] ; Latecki, Longin Jan [2]
Total Authors: 3
[1] Sao Paulo State Univ UNESP, Dept Stat Appl Math & Comp DEMAC, BR-13506900 Rio Claro - Brazil
[2] Temple Univ, Dept Comp & Informat Sci, Philadelphia, PA 19122 - USA
Total Affiliations: 2
Document type: Journal article
Source: JOURNAL OF IMAGING; v. 7, n. 3 MAR 2021.
Web of Science Citations: 0

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. (AU)

FAPESP's process: 20/11366-0 - Support for computational environments and experiments execution: weakly-supervised and classification fusion methods
Grantee:Lucas Pascotti Valem
Support Opportunities: Scholarships in Brazil - Technical Training Program - Technical Training
FAPESP's process: 18/15597-6 - Aplication and investigation of unsupervised learning methods in retrieval and classification tasks
Grantee:Daniel Carlos Guimarães Pedronette
Support Opportunities: Research Grants - Young Investigators Grants - Phase 2
FAPESP's process: 17/25908-6 - Weakly supervised learning for compressed video analysis on retrieval and classification tasks for visual alert
Grantee:João Paulo Papa
Support Opportunities: Research Grants - Research Partnership for Technological Innovation - PITE