Saliency Detection: A Self-Adaption Sparse Representation Approach

2011 ◽  
Author(s):  
Gaoxiang Zhang ◽  
Feng Jiang ◽  
Debin Zhao ◽  
Xiaoshuai Sun ◽  
Shaohui Liu
Keyword(s):  
Sparse Representation ◽  
Saliency Detection

Bottom-up saliency detection with sparse representation of learnt texture atoms

2016 ◽  
Vol 60 ◽  
pp. 348-360 ◽  
Author(s):  
Mai Xu ◽  
Lai Jiang ◽  
Zhaoting Ye ◽  
Zulin Wang
Keyword(s):  
Sparse Representation ◽  
Saliency Detection ◽  
Bottom Up

Saliency detection based on short-term sparse representation

2010 ◽  
Author(s):  
Xiaoshuai Sun ◽  
Hongxun Yao ◽  
Rongrong Ji ◽  
Pengfei Xu ◽  
Xianming Liu ◽  
...  
Keyword(s):  
Sparse Representation ◽  
Saliency Detection ◽  
Short Term

Image Super-Resolution via Saliency Sparse Representation

2014 ◽  
Vol 568-570 ◽  
pp. 659-662
Author(s):  
Xue Jun Zhang ◽  
Bing Liang Hu
Keyword(s):  
High Resolution ◽  
Sparse Representation ◽  
Saliency Detection ◽  
Computational Cost ◽  
Super Resolution ◽  
Compact Representation ◽  
High Resolution Images ◽  
Image Super Resolution ◽  
Single Image Super Resolution ◽  
Marked Area

The paper proposes a new approach to single-image super resolution (SR), which is based on sparse representation. Previous researchers just focus on the global intensive patch, without local intensive patch. The performance of dictionary trained by the local saliency intensive patch is more significant. Motivated by this, we joined the saliency detection to detect marked area in the image. We proposed a sparse representation for saliency patch of the low-resolution input, and used the coefficients of this representation to generate the high-resolution output. Compared to precious approaches which simply sample a large amount of image patch pairs, the saliency dictionary pair is a more compact representation of the patch pairs, reducing the computational cost substantially. Through the experiment, we demonstrate that our algorithm generates high-resolution images that are competitive or even superior in quality to images produced by other similar SR methods.

Sparse Representation of the Human Vision Information and the Saliency Detection Algorithm

2014 ◽  
Vol 513-517 ◽  
pp. 3349-3353
Author(s):  
Ju Bo Jin ◽  
Yu Xi Liu
Keyword(s):  
Firing Rate ◽  
Sparse Representation ◽  
Large Scale ◽  
Saliency Detection ◽  
Visual Saliency ◽  
Detection Algorithm ◽  
Human Vision ◽  
Short Term ◽  
Natural Statistics ◽  
Sparse Features

Representation and measurement are two important issues for saliency models. Different with previous works that learnt sparse features from large scale natural statistics, we propose to learn features from short-term statistics of single images. For saliency measurement, we defined basic firing rate (BFR) for each sparse feature, and then we propose to use feature activity rate (FAR) to measure the bottom-up visual saliency. The proposed FAR measure is biological plausible and easy to compute and with satisfied performance. Experiments on human trajectory positioning and psychological patterns demonstrate the effectiveness and robustness of our proposed method.

Spatiotemporal Saliency Detection via Sparse Representation

2012 ◽  
Author(s):  
Zhixiang Ren ◽  
Shenghua Gao ◽  
Deepu Rajan ◽  
Liang-Tien Chia ◽  
Yun Huang
Keyword(s):  
Sparse Representation ◽  
Saliency Detection

scholarly journals Detecting Saliency in Infrared Images via Multiscale Local Sparse Representation and Local Contrast Measure

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Xin Wang ◽  
Chunyan Zhang ◽  
Chen Ning ◽  
Yuzhen Zhang ◽  
Guofang Lv
Keyword(s):  
Sparse Representation ◽  
Background Noise ◽  
Saliency Detection ◽  
Infrared Image ◽  
Saliency Map ◽  
Local Contrast ◽  
Infrared Images ◽  
Saliency Maps ◽  
Contrast Measure ◽  
Local Sparse Representation

For infrared images, it is a formidable challenge to highlight salient regions completely and suppress the background noise effectively at the same time. To handle this problem, a novel saliency detection method based on multiscale local sparse representation and local contrast measure is proposed in this paper. The saliency detection problem is implemented in three stages. First, a multiscale local sparse representation based approach is designed for detecting saliency in infrared images. Using it, multiple saliency maps with various scales are obtained for an infrared image. These maps are then fused to generate a combined saliency map, which can highlight the salient region fully. Second, we adopt a local contrast measure based technique to process the infrared image. It divides the image into a number of image blocks. Then these blocks are utilized to calculate the local contrast to generate a local contrast measure based saliency map. In this map, the background noise can be suppressed effectually. Last, to make full use of the advantages of the above two saliency maps, we propose combining them together using an adaptive fusion scheme. Experimental results show that our method achieves better performance than several state-of-the-art algorithms for saliency detection in infrared images.

Saliency detection via image sparse representation and color features combination

2020 ◽  
Vol 79 (31-32) ◽  
pp. 23147-23159
Author(s):  
Xufan Zhang ◽  
Yong Wang ◽  
Zhenxing Chen ◽  
Jun Yan ◽  
Dianhong Wang
Keyword(s):  
Sparse Representation ◽  
Saliency Detection ◽  
Color Features
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