Ranking Graph Embedding for Learning to Rerank

Abstract

Dimensionality reduction is a key step to improving the generalization ability of reranking in image search. However, existing dimensionality reduction methods are typically designed for classification, clustering, and visualization, rather than for the task of learning to rank. Without using of ranking information such as relevance degree labels, direct utilization of conventional dimensionality reduction methods in ranking tasks generally cannot achieve the best performance. In this paper, we show that introducing ranking information into dimensionality reduction significantly increases the performance of image search reranking. The proposed method transforms graph embedding, a general framework of dimensionality reduction, into ranking graph embedding (RANGE) by modeling the global structure and the local relationships in and between different relevance degree sets, respectively. The proposed method also defines three types of edge weight assignment between two nodes: binary, reconstruction, and global. In addition, a novel principal components analysis based similarity calculation method is presented in the stage of global graph construction. Extensive experimental results on the MSRA-MM database demonstrate the effectiveness and superiority of the proposed RANGE method and the image search reranking framework.