  Single feature is inefficient to describe content of an image, which is a
shortcoming in traditional image retrieval task. We know that one image can be
described by different features. Multi-feature fusion ranking can be utilized
to improve the ranking list of query. In this paper, we first analyze graph
structure and multi-feature fusion re-ranking from manifold aspect. Then, Three
Tiers Neighborhood Graph (TTNG) is constructed to re-rank the original ranking
list by single feature and to enhance precision of single feature. Furthermore,
we propose Multi-graph Fusion Ranking (MFR) for multi-feature ranking, which
considers the correlation of all images in multiple neighborhood graphs.
Evaluations are conducted on UK-bench, Corel-1K, Corel-10K and Cifar-10
benchmark datasets. The experimental results show that our TTNG and MFR
outperform than other state-of-the-art methods. For example, we achieve
competitive results N-S score 3.91 and precision 65.00% on UK-bench and
Corel-10K datasets respectively.
