Formalization and management of content-based information retrieval of complex objects with hierarchical and cross-modal data

• Grant number: 14/25125-3
• Support type: Scholarships in Brazil - Doctorate
• Effective date (Start): April 01, 2015
• Effective date (End): March 31, 2019
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computer Systems
• Cooperation agreement: Coordination of Improvement of Higher Education Personnel (CAPES)
• Principal researcher: Agma Juci Machado Traina
• Grantee: Daniel Yoshinobu Takada Chino
• Home Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil
• Associated scholarship(s): 15/13346-8 - Content-based Information Retrieval in Social Media Services, BE.EP.DR

Abstract

Image based reports, such as papers, web pages and thesis have been digitally stored, with fast growing in volume and complexity. This scenario challenges both database management systems' efficiency and effectiveness. Big data addresses issues found when dealing with great data volume, scope, complexity and distribution. On the other hand, as complexity grows, new query methods are needed - numeric data and short strings are not enough to represent complex data. Similarity search has been proven to be the state-of-the-art to compare complex data. However, the concept of similarity itself becomes unclear when objects under comparison are composed of parts that can be compared by similarity as well, i.e., composite objects. There are several studies covering the similarity search over a single object, as images, audio and genetic sequence. However, currently there are no methods on the literature that deal with the similarity of composite objects. Moreover, similarity is defined by considering the semantic meaning to treat few data. This project aims to develop techniques to compare composite and complex objects that can be hierarchically organized regarding their similarity. The proposed techniques will be evaluated in different domains, as medical exams with several images; digital library, aiding recommender systems of academic thesis. (AU)

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)

CHINO, DANIEL Y. T.; SCABORA, LUCAS C.; CAZZOLATO, MIRELA T.; JORGE, ANA E. S.; TRAINA-, JR., CAETANO; TRAINA, AGMA J. M.. Segmenting skin ulcers and measuring the wound area using deep convolutional networks. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, v. 191, JUL 2020. (16/17330-1, 14/25125-3, 18/24414-2, 16/17078-0)

Academic Publications

(References retrieved automatically from State of São Paulo Research Institutions)

CHINO, Daniel Yoshinobu Takada. Managing feature extraction, mining and retrieval of complex data: applications in emergency situations and medicine. 2019. Doctoral Thesis - Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação (ICMC/SB) São Carlos.