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Multiple-instance image ranking for sketch-based image retrieval

Grant number: 15/26050-0
Support type:Scholarships abroad - Research Internship - Scientific Initiation
Effective date (Start): March 01, 2016
Effective date (End): June 30, 2016
Field of knowledge:Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
Principal Investigator:Moacir Antonelli Ponti
Grantee:Leonardo Sampaio Ferraz Ribeiro
Supervisor abroad: John Collomosse
Home Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil
Local de pesquisa : University of Surrey, England  
Associated to the scholarship:14/14557-0 - Image segmentation and feature extraction by regions for the creation of multi-instance learning scenarios, BP.IC

Abstract

There are image classification problems in which each image is represented by regions of interest; for each region a series of features can be extracted. As a result a set of feature vectors are available, and it is necessary to assign a label to this set of instances. The Multiple-Instance Learning (MIL) studies the problem in which each object is described as a bag (set of instances). This project aims to study the properties of MIL and develop solutions to image ranking for image retrieval using this approach; more specifically, we want to investigate this approach on sketch-based image retrieval (SBIR) tasks. We seek a solution that suits each query accordingly by performing instance selection on the bag's instances (that represent each image on the training sample) and presenting those based on a relevance ranking model based on the current query. Therefore, the following important aspects inherent to this project are: Multiple Instance Learning, feature extraction over sketches, image ranking and the instance selection.

Scientific publications
(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)
BUI, T.; RIBEIRO, L.; PONTI, M.; COLLOMOSSE, J. Compact descriptors for sketch-based image retrieval using a triplet loss convolutional neural network. COMPUTER VISION AND IMAGE UNDERSTANDING, v. 164, n. SI, p. 27-37, NOV 2017. Web of Science Citations: 10.

Please report errors in scientific publications list by writing to: cdi@fapesp.br.