Multistage PCA Whitening: A Robust Method to Dimensionality Reduction in Image Retrieval

Generating compact and robust feature representations using principal component analysis (PCA) is crucial for image retrieval tasks. However, most existing methods require PCA parameters to be learned from auxiliary datasets, which inevitably increases computational cost and limits generalization. To address this issue, we propose a novel dimensionality reduction learning method, namely multistage PCA whitening (MSPW), for image retrieval. Three main highlights are: 1) we propose a feature self-learning (FSL) method to learn PCA whitening (PW) parameters. This method can reconstruct the features of the retrieval dataset via singular value decomposition (SVD) and noise perturbation, which eliminates dependence on auxiliary datasets and alleviates performance degradation in high-dimensional features; 2) unlike existing single-stage dimensionality reduction methods, we introduce an online query self-learning (QSL) method that dynamically learns PCA parameters by incorporating query features, significantly improving the retrieval performance of using short-vector features; and 3) we propose a feature fusion (FF) method via using dimensional weighting to balance the contributions of various heterogeneous features, thereby enhancing the robustness of features across different dimensions. Experimental results on six benchmark datasets demonstrated that our MSPW method significantly outperforms existing state-of-the-art methods used for dimensionality reduction. Notably, our MSPW method using 4-D features can achieve more than 10% relative improvement over the previous best methods in terms of mean average precision (mAP) on two large-scale datasets.