dc.contributor.author | Dourado, Icaro | |
dc.contributor.author | Torres, Ricardo Da Silva | |
dc.date.accessioned | 2024-01-04T09:09:00Z | |
dc.date.available | 2024-01-04T09:09:00Z | |
dc.date.created | 2021-12-23T14:25:09Z | |
dc.date.issued | 2021 | |
dc.identifier.isbn | 978-1-6654-4220-6 | |
dc.identifier.uri | https://hdl.handle.net/11250/3109748 | |
dc.description.abstract | This paper introduces Supervised Bag of Graphs (SBoG), a supervised vocabulary learning approach for multi-modal 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. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | IEEE | en_US |
dc.relation.ispartof | 2021 International Conference on Content-Based Multimedia Indexing (CBMI) | |
dc.title | Learning Vocabularies to Embed Graphs in Multimodal Rank Aggregation Tasks | en_US |
dc.title.alternative | Learning Vocabularies to Embed Graphs in Multimodal Rank Aggregation Tasks | en_US |
dc.type | Chapter | en_US |
dc.description.version | publishedVersion | en_US |
dc.identifier.cristin | 1971785 | |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 1 | |