Multiple-instance image ranking for sketch-based image retrieval

• Grant number: 15/26050-0
• Support Opportunities: Scholarships abroad - Research Internship - Scientific Initiation
• Start date: March 01, 2016
• End date: 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
• Institution abroad: 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. (AU)

Scientific publications

(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.)

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

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, p. 11-pg., 2017-11-01. (15/26050-0, 16/16111-4)