A visual saliency detection algorithm based on channel selecting in transform domains

Author(s):  
Shen Yifeng ◽  
Niu Yifeng ◽  
Shen Lincheng
2012 ◽  
Vol 239-240 ◽  
pp. 811-815
Author(s):  
Zhi Hai Sun ◽  
Teng Song ◽  
Wen Hui Zhou ◽  
Hua Zhang

Visual saliency detection has become an important step between computer vision and digital image processing. Recent methods almost form a computational model based on color, which are difficult to overcome the shortcoming with cluttered and textured background. This paper proposes a novel salient object detection algorithm integrating with region color contrast and histograms of oriented gradients (HoG). Extensively experiments show that our algorithm outperforms other state-of-art saliency methods, yielding higher precision and better recall rate, even lower mean absolution error.


2013 ◽  
Vol 2013 ◽  
pp. 1-9
Author(s):  
Yuantao Chen ◽  
Weihong Xu ◽  
Fangjun Kuang ◽  
Shangbing Gao

Image segmentation process for high quality visual saliency map is very dependent on the existing visual saliency metrics. It is mostly only get sketchy effect of saliency map, and roughly based visual saliency map will affect the image segmentation results. The paper had presented the randomized visual saliency detection algorithm. The randomized visual saliency detection method can quickly generate the same size as the original input image and detailed results of the saliency map. The randomized saliency detection method can be applied to real-time requirements for image content-based scaling saliency results map. The randomization method for fast randomized video saliency area detection, the algorithm only requires a small amount of memory space can be detected detailed oriented visual saliency map, the presented results are shown that the method of visual saliency map used in image after the segmentation process can be an ideal segmentation results.


2012 ◽  
Vol 48 (25) ◽  
pp. 1591-1593 ◽  
Author(s):  
Di Wu ◽  
Xiudong Sun ◽  
Yongyuan Jiang ◽  
Chunfeng Hou

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 71422-71434 ◽  
Author(s):  
Zhenguo Gao ◽  
Naeem Ayoub ◽  
Danjie Chen ◽  
Bingcai Chen ◽  
Zhimao Lu

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