Retargeting Low-Resolution Aerial Imagery by Distribution-Preserving Perceptual Feature Selection

Abstract

This work presents a novel low-resolution (LR) aerial image retargeting pipeline, wherein the key is an active perception learning coupled with a distribution-preserving feature selector. We focus on engineering perceptual and descriptive visual representations for optimally shrinking different regions inside each LR aerial photo. In particular, by mimicking how humans sequentially perceiving different salient regions, an active learning paradigm is deployed to divide an LR aerial image into a succinct set of attractive regions coupled with the remaining non-attractive regions. Theoretically, the deployed active learning paradigm ensures that the selected attractive regions can maximally reconstruct the target LR aerial image, which accurately captures human gaze allocation. Subsequently, a semi-supervised distribution-preserving feature selector (DPFS) is proposed to acquire high quality features from the above selected attractive regions. Noticeably, DPFS only require a small proportion of LR aerial images to be labeled. And the labeled/unlabeled sample distribution are optimally preserved during feature selection(FS). The acquired high quality features are finally used to learn a Gaussian mixture model (GMM) for retargeting. Plenty of empirical results have shown the superiority of the proposed algorithm.

Keywords

• Retargeting
• active learning
• preserve distribution
• selecting features
• low-resolution