Rank diffusion for context-based image retrieval

• Main Author: Pedronette, Daniel Carlos Guimarães [UNESP]
• Publication Date: 2016
• Other Authors: Torres, Ricardo Da S.
• Format: Conference object
• Language: eng
• Source: Repositório Institucional da UNESP
• Download full: http://dx.doi.org/10.1145/2911996.2912060; http://hdl.handle.net/11449/168817
• topic: Content-based image retrieval; Rank diffusion process; Unsupervised distance learning

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.