Sparse recognition via intra-class dictionary learning using visual saliency information

2016 ◽  
Vol 196 ◽  
pp. 70-81 ◽  
Author(s):  
Ji-xin Liu ◽  
Quan-sen Sun
Keyword(s):  
Dictionary Learning ◽  
Visual Saliency

Gastric Lymph nodes detection based on visual saliency and dictionary learning

2016 ◽  
Author(s):  
Nuo Tong ◽  
Shuiping Gou ◽  
Yao Yao ◽  
Chenjiao Wang ◽  
Jing Bai
Keyword(s):  
Lymph Nodes ◽  
Dictionary Learning ◽  
Visual Saliency

Saliency Detection Based on Multi-Scale Superpixel and Dictionary Learning

2014 ◽  
Vol 701-702 ◽  
pp. 348-351
Author(s):  
Gang Hou ◽  
He Xin Yan ◽  
Fan Zhang ◽  
Hui Rong Hou ◽  
Ming Zhang
Keyword(s):  
Dictionary Learning ◽  
Saliency Detection ◽  
Visual Saliency ◽  
Scale Space ◽  
Detection Algorithm ◽  
Saliency Map ◽  
Reconstruction Error ◽  
Multi Scale ◽  
Training Samples ◽  
Scale Spaces

In recent years, saliency detection has been gaining increasing attention since it could significantly boost many content-based multimedia applications. In this paper, we propose a visual saliency detection algorithm based on multi-scale superpixel and dictionary learning . Firstly, in each scale space, we extract the boundaries as the training samples to learn a dictionary through sparse coding and dictionary learning methods. Then, according to reconstruction error of each superpixel, the saliency map is generated for each scale of superpixel. Finally, some saliency maps from different scale spaces are fused together to generate the final saliency map. The experimental results show that the proposed algorithm can highlight the salient regions uniformly and performs better compared with the other five methods.

scholarly journals Super resolution reconstruction for medical image based on adaptive multi-dictionary learning and structural self-similarity

2019 ◽  
Vol 24 (sup1) ◽  
pp. 81-88 ◽  
Author(s):  
Fang Zhang ◽  
Yue Wu ◽  
Zhitao Xiao ◽  
Lei Geng ◽  
Jun Wu ◽  
...  
Keyword(s):  
Dictionary Learning ◽  
Medical Image ◽  
Super Resolution ◽  
Self Similarity
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