EMR: A Scalable Graph-Based Ranking Model for Content-Based Image Retrieval

Abstract

Graph-based ranking models have been widely applied in information retrieval area. In this paper, we focus on a well known graph-based model - the Ranking on Data Manifold model, or Manifold Ranking (MR). Particularly, it has been successfully applied to content-based image retrieval, because of its outstanding ability to discover underlying geometrical structure of the given image database. However, manifold ranking is computationally very expensive, which significantly limits its applicability to large databases especially for the cases that the queries are out of the database (new samples). We propose a novel scalable graph-based ranking model called Efficient Manifold Ranking (EMR), trying to address the shortcomings of MR from two main perspectives: scalable graph construction and efficient ranking computation. Specifically, we build an anchor graph on the database instead of a traditional k-nearest neighbor graph, and design a new form of adjacency matrix utilized to speed up the ranking. An approximate method is adopted for efficient out-of-sample retrieval. Experimental results on some large scale image databases demonstrate that EMR is a promising method for real world retrieval applications.

DOI: 10.1109/TKDE.2013.70

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Cite this paper

@article{Xu2015EMRAS, title={EMR: A Scalable Graph-Based Ranking Model for Content-Based Image Retrieval}, author={Bin Xu and Jiajun Bu and Chun Chen and Can Wang and Deng Cai and Xiaofei He}, journal={IEEE Transactions on Knowledge and Data Engineering}, year={2015}, volume={27}, pages={102-114} }