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Selection and combination of unsupervised learning Methdos for content-based image retrieval

Grant number: 17/02091-4
Support Opportunities:Scholarships in Brazil - Master
Start date: May 01, 2017
End date: March 31, 2019
Field of knowledge:Physical Sciences and Mathematics - Computer Science
Agreement: Coordination of Improvement of Higher Education Personnel (CAPES)
Principal Investigator:Daniel Carlos Guimarães Pedronette
Grantee:Lucas Pascotti Valem
Host Institution: Instituto de Geociências e Ciências Exatas (IGCE). Universidade Estadual Paulista (UNESP). Campus de Rio Claro. Rio Claro , SP, Brazil
Associated research grant:13/08645-0 - Re-Ranking and rank aggregation approaches for image retrieval tasks, AP.JP

Abstract

The evolution of the mobile devices and the consequent facilities in the processes of acquisition and sharing of visual content have caused a great growth in the collections of images. In this scenario, it becomes imperative to develop automatic methods that allow indexing, analyzing and searching in such content. Content-Based Image Retrieval (CBIR) are a promising solution. However, assessing the similarity between the visual content of images presents several challenges. Unsupervised Learning have firmed as a solution to improve the effectiveness of retrieval systems without requiring user intervention. As several methods have been developed using distinct and complementary approaches, new research challenges emerge regarding the combination of these methods in order to increase the gains. This research project intends to investigate and propose strategies for selection and aggregation the unsupervised learning methods. (AU)

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Scientific publications (11)
(The scientific publications listed on this page originate from the Web of Science or SciELO databases. Their authors have cited FAPESP grant or fellowship project numbers awarded to Principal Investigators or Fellowship Recipients, whether or not they are among the authors. This information is collected automatically and retrieved directly from those bibliometric databases.)
VALEM, LUCAS PASCOTTI; GUIMARAES PEDRONETTE, DANIEL CARLOS; ALMEIDA, JURANDY. Unsupervised similarity learning through Cartesian product of ranking references. PATTERN RECOGNITION LETTERS, v. 114, n. SI, p. 41-52, . (14/04220-8, 17/02091-4, 16/06441-7, 13/08645-0)
VALEM, LUCAS PASCOTTI; PEDRONETTE, DANIEL C. G.; BREVE, FABRICIO; GUILHERME, IVAN RIZZO; IEEE. Manifold Correlation Graph for Semi-Supervised Learning. 2018 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), v. N/A, p. 7-pg., . (16/05669-4, 13/08645-0, 17/02091-4)
VALEM, LUCAS PASCOTTI; GUIMARDES PEDRONETTE, DANIEL CARLOS; ASSOC COMP MACHINERY. An Unsupervised Genetic Algorithm Framework for Rank Selection and Fusion on Image Retrieval. ICMR'19: PROCEEDINGS OF THE 2019 ACM INTERNATIONAL CONFERENCE ON MULTIMEDIA RETRIEVAL, v. N/A, p. 5-pg., . (18/15597-6, 17/25908-6, 17/02091-4)
GUIMARAES PEDRONETTE, DANIEL CARLOS; VALEM, LUCAS PASCOTTI; ALMEIDA, JURANDY; TONES, RICARDO DA S.. Multimedia Retrieval Through Unsupervised Hypergraph-Based Manifold Ranking. IEEE Transactions on Image Processing, v. 28, n. 12, p. 5824-5838, . (14/50715-9, 16/50250-1, 17/25908-6, 17/20945-0, 14/12236-1, 16/06441-7, 18/15597-6, 13/50155-0, 17/02091-4, 15/24494-8)
VALEM, LUCAS PASCOTTI; GUIMARAES PEDRONETTE, DANIEL CARLOS; ALMEIDA, JURANDY. Unsupervised similarity learning through Cartesian product of ranking references. PATTERN RECOGNITION LETTERS, v. 114, p. 12-pg., . (16/06441-7, 14/04220-8, 13/08645-0, 17/02091-4)
VALEM, LUCAS PASCOTTI; GUIMARAES PEDRONETTE, DANIEL CARLOS; ACM. Selection and Combination of Unsupervised Learning Methods for Image Retrieval. PROCEEDINGS OF THE 15TH INTERNATIONAL WORKSHOP ON CONTENT-BASED MULTIMEDIA INDEXING (CBMI), v. N/A, p. 6-pg., . (17/02091-4, 13/08645-0)
CAMACHO PRESOTTO, JOAO GABRIEL; VALEM, LUCAS PASCOTTI; PEDRONETTE, DANIEL CARLOS GUIMARAES; VENTO, M; PERCANNELLA, G. Unsupervised Effectiveness Estimation Through Intersection of Ranking References. COMPUTER ANALYSIS OF IMAGES AND PATTERNS, CAIP 2019, PT II, v. 11679, p. 14-pg., . (18/15597-6, 17/25908-6, 17/02091-4)
VALEM, LUCAS PASCOTTI; DE OLIVEIRA, CARLOS RENAN; GUIMARAES PEDRONETTE, DANIEL CARLOS; ALMEIDA, JURANDY. Unsupervised Similarity Learning through Rank Correlation and kNN Sets. ACM Transactions on Multimedia Computing Communications and Applications, v. 14, n. 4, . (17/25908-6, 17/02091-4, 16/06441-7, 13/08645-0)
VALEM, LUCAS PASCOTTI; GUIMARAES PEDRONETTE, DANIEL CARLOS. Unsupervised selective rank fusion for image retrieval tasks. Neurocomputing, v. 377, p. 182-199, . (17/25908-6, 17/02091-4, 18/15597-6)
VALEM, LUCAS PASCOTTI; GUIMARAES PEDRONETTE, DANIEL CARLOS. Graph -based selective rank fusion for unsupervised image retrieval. PATTERN RECOGNITION LETTERS, v. 135, p. 82-89, . (17/25908-6, 13/08645-0, 17/02091-4, 18/15597-6)
ALMEIDA, JURANDY; VALEM, LUCAS P.; PEDRONETTE, DANIEL C. G.; BATTIATO, S; GALLO, G; SCHETTINI, R; STANCO, F. A Rank Aggregation Framework for Video Interestingness Prediction. IMAGE ANALYSIS AND PROCESSING,(ICIAP 2017), PT I, v. 10484, p. 12-pg., . (16/06441-7, 13/08645-0, 17/02091-4)
Academic Publications
(References retrieved automatically from State of São Paulo Research Institutions)
VALEM, Lucas Pascotti. Unsupervised Selective Rank Fusion for Content-based Image Retrieval.. 2019. Master's Dissertation - Universidade Estadual Paulista (Unesp). Instituto de Biociências Letras e Ciências Exatas. São José do Rio Preto São José do Rio Preto.