Noise Resistant Graph Ranking for Improved Web Image Search

In this paper, we exploit a novel ranking mechanism that processes query samples with noisy labels, motivated by the practical application of web image search re-ranking where the originally highest ranked images are usually posed as pseudo queries for subsequent re-ranking. Availing ourselves of the low-frequency spectrum of a neighborhood graph built on the samples, we propose a graphtheoretical framework amenable to noise resistant ranking. The proposed framework consists of two components: spectral filtering and graph-based ranking. The former leverages sparse bases, progressively selected from a pool of smooth eigenvectors of the graph Laplacian, to reconstruct the noisy label vector associated with the query sample set and accordingly filter out the query samples with less authentic positive labels. The latter applies a canonical graph ranking algorithm with respect to the filtered query sample set. Quantitative image re-ranking experiments carried out on two public web image...

• Type: Journal
• Year: 2011
• Where: CVPR
• Authors: Wei Liu, Yu-Gang Jiang, Jiebo Luo, Shih-Fu Chang
• Keywords: Computer Vision, CVPR 2011, Query Sample, Web Image, Web Image Search