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ROMIR: Robust Multi-View Image Re-Ranking
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DOI：10.1109/TKDE.2018.2876834.png)
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摘要
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In multi-view re-ranking, multiple heterogeneous visual features are usually projected onto a low-dimensional subspace, and thus the resulting latent representation can be used for the subsequent similarity-based ranking. Albeit effective, this standard mechanism underplays the intrinsic structure underlying the latent subspace and does not take into account the substantial noise in the original spaces. In this paper, we propose a robust multi-view image re-ranking strategy. Due to the dramatic variability in image visual appearance, it is necessary to uncover the shared components underlying those query-related instances that are visually unlike for improving the re-ranking accuracy. Consequently, it is reasonable to assume the latent subspace enjoys the low-rank property and thus the subspace recovery can be achieved via the low-rank modeling accordingly. In addition, since the real-world data are usually partially contaminated, we employ $\ell _{2, 1}$l2,1-norm based sparsity constraint to appropriately model the sample-specific mapping noise for enhancing the model robustness. In order to produce discriminative representations, we encode a similarity preserving term in our multi-view embedding framework. As a result, the sample separability is maximally maintained in the latent subspace with sufficient discriminative power. The extensive evaluations on public landmark benchmarks demonstrate the efficacy and superiority of the proposed method.
Keyword:
Visualization
Kernel
Encoding
Data models
Databases
Training
Robustness
Multi-view image re-ranking
low-rank modeling
sample-specific mapping noise
discriminative representations
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10.4
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Auto-Weighted Multi-View Learning for Image Clustering and Semi-Supervised Classification用于图像聚类和半监督分类的自动加权多视图学习
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分享收藏An image retrieval scheme with relevance feedback using feature reconstruction and SVM reclassification.png)
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