| 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. | |