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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.)

Unsupervised selective rank fusion for image retrieval tasks

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Author(s):
Valem, Lucas Pascotti [1] ; Guimaraes Pedronette, Daniel Carlos [1]
Total Authors: 2
Affiliation:
[1] Sao Paulo State Univ UNESP, Dept Stat Appl Math & Comp DEMAC, Rio Claro - Brazil
Total Affiliations: 1
Document type: Journal article
Source: Neurocomputing; v. 377, p. 182-199, FEB 15 2020.
Web of Science Citations: 0
Abstract

Several visual features have been developed for content-based image retrieval in the last decades, including global, local and deep learning-based approaches. However, despite the huge advances in features development and mid-level representations, a single visual descriptor is often insufficient to achieve effective retrieval results in several scenarios. Mainly due to the diverse aspects involved in human visual perception, the combination of different features has been establishing as a relevant trend in image retrieval. An intrinsic difficulty consists in the task of selecting the features to combine, which is often supported by supervised learning approaches. Therefore, in the absence of labeled data, selecting features in an unsupervised way is a very challenging, although essential task. In this paper, an unsupervised framework is proposed to select and fuse visual features in order to improve the effectiveness of image retrieval tasks. The framework estimates the effectiveness and correlation among features through a rank-based analysis and uses a list of ranker pairs to determine the selected features combinations. High-effective retrieval results were achieved through a comprehensive experimental evaluation conducted on 5 public datasets, involving 41 different features and comparison with other methods. Relative gains up to +55% were obtained in relation to the highest effective isolated feature. (c) 2019 Elsevier B.V. All rights reserved. (AU)

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 type: Research Grants - Research Partnership for Technological Innovation - PITE
FAPESP's process: 17/02091-4 - Selection and combination of unsupervised learning Methdos for content-based image retrieval
Grantee:Lucas Pascotti Valem
Support type: Scholarships in Brazil - Master
FAPESP's process: 18/15597-6 - Aplication and investigation of unsupervised learning methods in retrieval and classification tasks
Grantee:Daniel Carlos Guimarães Pedronette
Support type: Research Grants - Young Investigators Grants - Phase 2