DocumentCode :
77846
Title :
Low-Level Visual Saliency With Application on Aerial Imagery
Author :
Rigas, Ioannis ; Economou, George ; Fotopoulos, Spiros
Author_Institution :
Phys. Dept., Univ. of Patras, Patras, Greece
Volume :
10
Issue :
6
fYear :
2013
fDate :
Nov. 2013
Firstpage :
1389
Lastpage :
1393
Abstract :
In this letter, a method for the construction of low-level saliency maps is presented in tandem with their evaluation on a set of aerial images. One of the key inspirations for the current research lies on the observation that, usually, the most significant man-made structures in a wide-field aerial image resemble the low-level features that can be detected with a bottom-up saliency map. Aerial photography comprises, hence, a natural domain of application for a method that computationally models low-level saliency. With the employment of mechanisms analogous to the neural functions that drive human attention, we propose a bioinspired framework based on sparse coding for the extraction of information about saliency. The suggested algorithm is then evaluated on a novel data set that has been constructed with the utilization of aerial images and the corresponding manually designed ground truth binary maps of salient structures. The results demonstrate the efficiency of the proposed scheme to highlight conspicuous locations in aerial images, revealing the perspectives on the employment of low-level saliency maps in aerial imaging systems.
Keywords :
feature extraction; geophysical image processing; geophysical techniques; image reconstruction; remote sensing; aerial imaging systems; aerial photography; bioinspired framework; bottom-up saliency map; ground truth binary maps; information extraction; low-level features; low-level saliency maps; low-level visual saliency; neural functions; salient structures; significant man-made structures; sparse coding; wide-field aerial image; Algorithm design and analysis; Dictionaries; Encoding; Feature extraction; Image coding; Image color analysis; Visualization; Aerial imagery; low-level features; saliency; sparse coding;
fLanguage :
English
Journal_Title :
Geoscience and Remote Sensing Letters, IEEE
Publisher :
ieee
ISSN :
1545-598X
Type :
jour
DOI :
10.1109/LGRS.2013.2243402
Filename :
6472772
Link To Document :
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