A graph-based ranked-list model for unsupervised distance learning on shape retrieval
Several re-ranking algorithms have been proposed recently. Some effective approaches are based on complex graph-based diffusion processes, which usually are time consuming and therefore inappropriate for real-world large scale shape collections. In this paper, we introduce a novel graph-based approa...
| Autores: | , , |
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| Tipo de documento: | artigo |
| Estado: | Versão publicada |
| Data de publicação: | 2016 |
| País: | Brasil |
| Recursos: | Universidade Estadual Paulista (UNESP) |
| Repositório: | Repositório Institucional da UNESP |
| Idioma: | inglês |
| OAI Identifier: | oai:repositorio.unesp.br:11449/169114 |
| Acesso em linha: | http://dx.doi.org/10.1016/j.patrec.2016.05.021 http://hdl.handle.net/11449/169114 |
| Access Level: | Acceso aberto |
| Palavra-chave: | Graph-based approaches Ranking methods Shape retrieval |
| Resumo: | Several re-ranking algorithms have been proposed recently. Some effective approaches are based on complex graph-based diffusion processes, which usually are time consuming and therefore inappropriate for real-world large scale shape collections. In this paper, we introduce a novel graph-based approach for iterative distance learning in shape retrieval tasks. The proposed method is based on the combination of graphs defined in terms of multiple ranked lists. The efficiency of the method is guaranteed by the use of only top positions of ranked lists in the definition of graphs that encode reciprocal references. Effectiveness analysis performed in three widely used shape datasets demonstrate that the proposed graph-based ranked-list model yields significant gains (up to +55.52%) when compared with the use of shape descriptors in isolation. Furthermore, the proposed method also yields comparable or superior effectiveness scores when compared with several state-of-the-art approaches. |
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