Detection of regions of interest in a high-spatial-resolution remote sensing image based on an adaptive spatial subsampling visual attention model

2013 ◽  
Vol 50 (1) ◽  
pp. 112-132 ◽  
Author(s):  
Libao Zhang ◽  
Hao Li ◽  
Pengfei Wang ◽  
Xianchuan Yu
Keyword(s):  
Remote Sensing ◽  
Visual Attention ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Remote Sensing Image ◽  
Regions Of Interest ◽  
Visual Attention Model ◽  
Attention Model

scholarly journals Remote Sensing Image Retrieval with Combined Features Of Salient Region

2014 ◽  
Vol XL-6 ◽  
pp. 83-88 ◽  
Author(s):  
Z. F. Shao ◽  
W. X. Zhou ◽  
Q. M. Cheng
Keyword(s):  
Remote Sensing ◽  
Visual Attention ◽  
Image Retrieval ◽  
Texture Features ◽  
Remote Sensing Image ◽  
Experimental Results ◽  
Semantic Gap ◽  
Visual Attention Model ◽  
Attention Model ◽  
Combined Features

Low-level features tend to achieve unsatisfactory retrieval results in remote sensing image retrieval community because of the existence of semantic gap. In order to improve retrieval precision, visual attention model is used to extract salient objects from image according to their saliency. Then color and texture features are extracted from salient objects and regarded as feature vectors for image retrieval. Experimental results demonstrate that our method improves retrieval results and obtains higher precision.

Regions of Interest Detection Algorithm Based on Improved Visual Attention Model

2014 ◽  
Vol 513-517 ◽  
pp. 3368-3371 ◽  
Author(s):  
Zhen Hui Xu ◽  
Jun Yang ◽  
Wan Jun Zhang ◽  
Zhen Jun Yang
Keyword(s):  
Visual Attention ◽  
Target Position ◽  
Detection Algorithm ◽  
Regions Of Interest ◽  
Moving Target ◽  
Visual Attention Model ◽  
Attention Model ◽  
New Thinking ◽  
Interest Detection ◽  
Improved Model

Regions of Interest (ROI) detection algorithm based on Visual Attention Model can rapidly focus the attention in the conspicuous target region, and extract the interested region. As to some complicated scenes, it is very difficult to detect the target accurately by using general target detecting method, but using Regions of Interest detection algorithm based on Itti Visual Attention Model can detect the target position very well. But pay attention to Itti Visual Attention Model, it utilizes the luminance, color and texture character of the target to detect the position of it. As little moving target, these characters of it are not obvious, so the detecting result is not satisfactory that utilizing Itti Visual Attention Model directly. According to the problem, this text proposes one Regions of Interest detection algorithm on the basis of improved Itti visual attention model by introducing movement character. The experiment shows that the improved model puts forward a new thinking of little moving target detection.

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