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Multiple-instance image ranking for sketch-based image retrieval

Grant number: 15/26050-0
Support Opportunities: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:Leo Sampaio Ferraz Ribeiro
Supervisor: John Collomosse
Host Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil
Research place: 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


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. (AU)

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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, . (15/26050-0, 16/16111-4)

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