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

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. [ABSTRACT FROM AUTHOR]