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Aplication and investigation of unsupervised learning methods in retrieval and classification tasks


Unsupervised Learning methods have been established as a solution to increase the effectiveness of content-based searches without requiring user intervention. These methods exploit contextual relationship among images, usually encoded in the distance/similarity information of the collections.This research project intends to investigate the application of such methods in new and diversified domains. Unsupervised learning methods reevaluate the similarity between the elements of the collection and can be used as a pre-processing step in classification tasks. In addition, initial results indicate that the methods can be applied in general multimedia and multimodal retrieval scenarios, considering audio and video.Therefore, the central objective of the proposed project is to deepen such investigation, expanding the domains of application of unsupervised learning. methods. (AU)

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Scientific publications (8)
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
CAMPOS, VICTOR DE ABREU; GUIMARAES PEDRONETTE, DANIEL CARLOS. A framework for speaker retrieval and identification through unsupervised learning. COMPUTER SPEECH AND LANGUAGE, v. 58, p. 153-174, . (17/25908-6, 15/07934-4, 18/15597-6)
GUIMARAES PEDRONETTE, DANIEL CARLOS; VALEM, LUCAS PASCOTTI; TORRES, RICARDO DA S.. A BFS-Tree of ranking references for unsupervised manifold learning. PATTERN RECOGNITION, v. 111, . (16/50250-1, 15/24494-8, 13/50155-0, 18/15597-6, 13/50169-1, 17/20945-0, 14/12236-1, 17/25908-6, 14/50715-9)
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. 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)
GUIMARAES PEDRONETTE, DANIEL CARLOS; LATECKI, LONGIN JAN. Rank-based self-training for graph convolutional networks. INFORMATION PROCESSING & MANAGEMENT, v. 58, n. 2, . (17/25908-6, 18/15597-6)
GUIMARAES PEDRONETTE, DANIEL CARLOS; PASCOTTI VALEM, LUCAS; LATECKI, LONGIN JAN. Efficient Rank-Based Diffusion Process with Assured Convergence. JOURNAL OF IMAGING, v. 7, n. 3, . (20/11366-0, 18/15597-6, 17/25908-6)
ROZIN, BIONDA; PEREIRA-FERRERO, VANESSA HELENA; LOPES, LEONARDO TADEU; PEDRONETTE, DANIEL CARLOS GUIMARAES. A rank-based framework through manifold learning for improved clustering tasks. INFORMATION SCIENCES, v. 580, p. 202-220, . (17/25908-6, 20/02183-9, 20/08854-2, 18/15597-6)

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