Rank diffusion for context-based image retrieval
Nenhuma Miniatura disponível
Data
2016-06-06
Autores
Pedronette, Daniel Carlos Guimarães [UNESP]
Torres, Ricardo Da S.
Título da Revista
ISSN da Revista
Título de Volume
Editor
Resumo
This paper presents an efficient diffusion-based re-ranking approach. The proposed method propagates contextual information defined in terms of top-ranked objects of ranked lists in a diffusion process. That makes the method suitable for large scale real-world collections. Experiments were conducted considering public image collections, several descriptors, and comparisons with state-of-the-art methods. Experimental results demonstrate that the proposed method provides high effectiveness gains with low computational costs.
Descrição
Palavras-chave
Content-based image retrieval, Rank diffusion process, Unsupervised distance learning
Como citar
ICMR 2016 - Proceedings of the 2016 ACM International Conference on Multimedia Retrieval, p. 321-325.