| Summary: | Recognizing the multiple categories of an high-resolution (HR) aerial photos is an indispensable technique in geoscience and remote sensing. In this work, a perceptual low-rank algorithm combined with a geometry-preserving feature selection (FS) is proposed for categorizing HR aerial photos. In practice, the theory of human visual perception indicates that for each scenery, the background non-salient regions are highly correlated, whereas the foreground visually/semantically salient regions are almost uncorrelated. Motivated by this, we design a novel low-rank algorithm that seeks a sparse set of foreground visually/semantically salient image patches. These patches are sequentially linked into a so- called GSP (path reflecting gaze movement) to mimick human vision system. Afterward, a geometry-preserving FS algorithm is proposed to select highly discriminative features from the aforementioned gaze features, wherein a classifier can be trained simultaneously. Comprehensive experimental validation on our Internet-scale image set have shown its superiority.
|
|---|