A hybrid relevant-diverse approach for image re-ranking with multiple features

Abstract

In this paper, we present a hybrid relevant-diverse image re-ranking approach that combines the strengths of two previous methods: the reciprocal election algorithm proposed by R. van Leuken et al. and the greedy search algorithm proposed by T. Deselaers et al. Our approach is a cluster-based re-ranking method. We select several candidate representatives based on the reciprocal election algorithm and employ a bounded greedy search algorithm to find the most relevant-diverse one as the cluster representative. We fuse multiple features to calculate image similarity, including colour and shape and especially topic content features, and discuss the benefits of integrating different features. In addition, we quantitatively evaluate our approach on a real-world Web image data set and obtain results which outperform the state of the art.

Keywords:

• image retrieval
• re-rank
• diversity
• cluster
• multiple visual features

2010 AMS Subject Classification :

• 68U10

1998 ACM Computing Classifications :

• H.3.3
• I.5.3

Acknowledgements

This work was supported by the Natural Science Foundation of China (60970047), the Natural Science Foundation of Shandong Province (Y2008G19), the Key Science-Technology Project of Shandong Province (2008GG10001026) and China Postdoctoral Science Foundation (20100471503).

Notes

http://people.csail.mit.edu/fergus/iccv2005/bagwords.html

http://gibbslda.sourceforge.net/