Learning Vocabularies to Embed Graphs in Multimodal Rank Aggregation Tasks

Author(s)

Dourado, Icaro Cavalcante ; Tones, Ricardo da Silva ; IEEE

• Document type: Journal article
• Source: 2021 INTERNATIONAL CONFERENCE ON CONTENT-BASED MULTIMEDIA INDEXING (CBMI); v. N/A, p. 6-pg., 2021-01-01.

Abstract

This paper introduces Supervised Bag of Graphs (SBoG), a supervised vocabulary learning approach for multimodal graph-based rank aggregation tasks. In our formulation, collection objects are represented based on complementary views provided by different ranks, defined in terms of multiple modalities. Ranks are encoded into a graph (fusion graph), which is later embedded into a vector representation (fusion vector), based on a vocabulary of graph words. SBoG explores different strategies for exploring collection labels to define suitable vocabularies that lead to effective representations. Experiments considered the use of SBoG-based representations in multimedia classification tasks. Obtained results demonstrate that SBoG leads to gains up to 28% when compared with state-of-the-art and traditional approaches. (AU)

• FAPESP's process: 16/50250-1 - The secret of playing football: Brazil versus the Netherlands
• Grantee: Sergio Augusto Cunha
• Support Opportunities: Research Projects - Thematic Grants
• FAPESP's process: 14/12236-1 - AnImaLS: Annotation of Images in Large Scale: what can machines and specialists learn from interaction?
• Grantee: Alexandre Xavier Falcão
• Support Opportunities: Research Projects - Thematic Grants
• FAPESP's process: 17/20945-0 - Multi-user equipment approved in great 16/50250-1: local positioning system
• Grantee: Sergio Augusto Cunha
• Support Opportunities: Multi-user Equipment Program
• FAPESP's process: 15/24494-8 - Communications and processing of big data in cloud and fog computing
• Grantee: Nelson Luis Saldanha da Fonseca
• Support Opportunities: Research Projects - Thematic Grants