ROMIR: Robust Multi-view Image Re-ranking

Abstract

Recently, multi-view features have significantly pro- moted the performance of image re-ranking by providing complementary image descriptions. Without loss of generality, in multi-view re-ranking, multiple heterogeneous visual features of high dimensionality are 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 multi-view feature spaces. In this paper, we propose a robust multi-view feature learning strategy for accurate image re-ranking. Due to the dramatic variability in visual appearance for different target images, 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, the real-world data are usually partially contaminated and it is required to appropriately model the sample-dependent data noise. Towards this end, we employ l2,1- norm based sparsity constraint to 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 experimental evaluations on public landmark benchmarks reveal that our approach achieves impressive performance superior to the state-of-the-art, which thus demonstrates the efficacy of the proposed method.