Please use this identifier to cite or link to this item: https://doi.org/10.1145/2072298.2071966
Title: Difficulty guided image retrieval using linear multiview embedding
Authors: Li, Y.
Geng, B.
Zha, Z.-J. 
Tao, D.
Yang, L.
Xu, C.
Keywords: Query difficulty estimation
Reranking
Spectral embedding
Issue Date: 2011
Source: Li, Y.,Geng, B.,Zha, Z.-J.,Tao, D.,Yang, L.,Xu, C. (2011). Difficulty guided image retrieval using linear multiview embedding. MM'11 - Proceedings of the 2011 ACM Multimedia Conference and Co-Located Workshops : 1169-1172. ScholarBank@NUS Repository. https://doi.org/10.1145/2072298.2071966
Abstract: Existing image retrieval systems suffer from a radical performance variance for different queries. The bad initial search results for "difficult" queries may greatly degrade the performance of their subsequent refinements, especially the refinement that utilizes the information mined from the search results, e.g., pseudo relevance feedback based reranking. In this paper, we tackle this problem by proposing a query difficulty guided image retrieval system, which selectively performs reranking according to the estimated query difficulty. To improve the performance of both reranking and difficulty estimation, we apply multiview embedding (ME) to images represented by multiple different features for integrating a joint subspace by preserving the neighborhood information in each feature space. However, existing ME approaches suffer from both "out of sample" and huge computational cost problems, and cannot be applied to online reranking or offline large-scale data processing for practical image retrieval systems. Therefore, we propose a linear multiview embedding algorithm which learns a linear transformation from a small set of data and can effectively infer the subspace features of new data. Empirical evaluations on both Oxford and 500K ImageNet datasets suggest the effectiveness of the proposed difficulty guided retrieval system with LME. Copyright 2011 ACM.
Source Title: MM'11 - Proceedings of the 2011 ACM Multimedia Conference and Co-Located Workshops
URI: http://scholarbank.nus.edu.sg/handle/10635/41363
ISBN: 9781450306164
DOI: 10.1145/2072298.2071966
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