Noise resistant graph ranking for improved web image search
Noise resistant graph ranking for improved web image search
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DOI：
10.1109/cvpr.2011.5995315
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发表时间：
2011-06
期刊：
影响因子：
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通讯作者：
W. Liu;Yu-Gang Jiang;Jiebo Luo;Shih-Fu Chang
中科院分区：
文献类型：
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作者：
W. Liu;Yu-Gang Jiang;Jiebo Luo;Shih-Fu Chang
在本文中，我们开发了一种新的排名机制，处理查询样本与嘈杂的标签，动机的实际应用中的Web图像搜索重新排名，原来排名最高的图像通常是作为伪查询后续重新排名。我们将建立在样本上的邻域图的低频频谱作为我们自己的参考，提出了一个适合抗噪声排名的图论框架。该框架由两部分组成：光谱过滤和基于图的排名。前者利用稀疏基，从图拉普拉斯算子的平滑特征向量池中逐步选择，以重建与查询样本集相关联的噪声标签向量，并相应地过滤掉具有不太真实的正标签的查询样本。后者针对过滤后的查询样本集应用规范图排序算法。在两个公共网络图像数据库上进行的定量图像重排序实验证明，我们的重排序方法与最先进的方法相比毫不逊色，并大大提高了网络图像搜索引擎的性能，尽管我们从这些搜索引擎返回的排名靠前的图像中收集了噪声查询。
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 graph-theoretical 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 databases bear out that our re-ranking approach compares favorably with the state-of-the-arts and improves web image search engines by a large margin though we harvest the noisy queries from the top-ranked images returned by these search engines.