Content-Based re-ranking of text-based image search results
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
Query-dependent visual dictionary adaptation for image reranking
Proceedings of the 21st ACM international conference on Multimedia
Semi-supervised learning with manifold fitted graphs
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
| Hi-index | 0.00 |
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.