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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.)

Multimedia Retrieval Through Unsupervised Hypergraph-Based Manifold Ranking

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Guimaraes Pedronette, Daniel Carlos [1] ; Valem, Lucas Pascotti [1] ; Almeida, Jurandy [2] ; Tones, Ricardo da S. [3]
Total Authors: 4
[1] State Univ Sao Paulo, Dept Stat Appl Maths & Comp, BR-13506900 Rio Claro - Brazil
[2] Univ Fed Sao Paulo, Inst Sci & Technol, BR-12231280 Sao Jose Dos Campos - Brazil
[3] Univ Estadual Campinas, Inst Comp, RECOD Lab, BR-13083852 Campinas, SP - Brazil
Total Affiliations: 3
Document type: Journal article
Source: IEEE Transactions on Image Processing; v. 28, n. 12, p. 5824-5838, DEC 2019.
Web of Science Citations: 0

Accurately ranking images and multimedia objects are of paramount relevance in many retrieval and learning tasks. Manifold learning methods have been investigated for ranking mainly due to their capacity of taking into account the intrinsic global manifold structure. In this paper, a novel manifold ranking algorithm is proposed based on the hypergraphs for unsupervised multimedia retrieval tasks. Different from traditional graph-based approaches, which represent only pairwise relationships, hypergraphs are capable of modeling similarity relationships among a set of objects. The proposed approach uses the hyperedges for constructing a contextual representation of data samples and exploits the encoded information for deriving a more effective similarity function. An extensive experimental evaluation was conducted on nine public datasets including diverse retrieval scenarios and multimedia content. Experimental results demonstrate that high effectiveness gains can be obtained in comparison with the state-of-the-art methods. (AU)

FAPESP's process: 14/50715-9 - Characterizing and predicting biomass production in sugarcane and eucalyptus plantations in Brazil
Grantee:Rubens Augusto Camargo Lamparelli
Support Opportunities: Research Grants - Research Partnership for Technological Innovation - PITE
FAPESP's process: 16/50250-1 - The secret of playing football: Brazil versus the Netherlands
Grantee:Sergio Augusto Cunha
Support Opportunities: Research Projects - Thematic Grants
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
FAPESP's process: 17/20945-0 - Multi-user equipment approved in great 16/50250-1: local positioning system
Grantee:Sergio Augusto Cunha
Support Opportunities: Multi-user Equipment Program
FAPESP's process: 14/12236-1 - AnImaLS: Annotation of Images in Large Scale: what can machines and specialists learn from interaction?
Grantee:Alexandre Xavier Falcão
Support Opportunities: Research Projects - Thematic Grants
FAPESP's process: 16/06441-7 - Semantic information retrieval in large video databases
Grantee:Jurandy Gomes de Almeida Junior
Support Opportunities: Regular Research Grants
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: 13/50155-0 - Combining new technologies to monitor phenology from leaves to ecosystems
Grantee:Leonor Patricia Cerdeira Morellato
Support Opportunities: Research Program on Global Climate Change - University-Industry Cooperative Research (PITE)
FAPESP's process: 17/02091-4 - Selection and combination of unsupervised learning Methdos for content-based image retrieval
Grantee:Lucas Pascotti Valem
Support Opportunities: Scholarships in Brazil - Master
FAPESP's process: 15/24494-8 - Communications and processing of big data in cloud and fog computing
Grantee:Nelson Luis Saldanha da Fonseca
Support Opportunities: Research Projects - Thematic Grants