Feature fusion is an effective solution for improving image retrieval
performance. Although the more feature types, the better accuracy,
complexity also increases. Applications in practice typically afford a
limited number of feature types. Due to the strong complementarity,
global and local features form an ideal combination for many fusion
applications. However, the two kinds of features are intrinsically
different in nature, thus cannot be fused in a straightforward way. In
this work, we propose an integrated image retrieval and feature fusion
framework for global and local features. It is based on inverted index
fusion, a technique for efficient image retrieval. The core idea is to
rank candidates by weighted voting during candidate selection, which is
named pre-ranking. This procedure takes place before re-ranking, and is
potentially superior to conventional late fusion. Extensive experiments
on three public datasets show that the light-weight pre-ranking stage
significantly contributes to accuracy, and brings substantial
improvement when used together with re-ranking. Our method is robust and
versatile, and can be applied to any scenario where inverted indexing is
used. It is a promising technique for multimedia retrieval in the big
data era.