Image Retargeting Based on a New Salient Region Detection Method

2015 ◽  
Author(s):  
Jing Li ◽  
Weiwei Wang ◽  
Xiaoping Li
Keyword(s):  
Detection Method ◽  
Image Retargeting ◽  
Salient Region Detection ◽  
Salient Region ◽  
Region Detection

Multi-Resolution Salient Region Detection in Frequency Domain

2014 ◽  
Vol 6 (1) ◽  
pp. 60-70
Author(s):  
Yingchun Guo ◽  
Yanhong Feng ◽  
Gang Yan ◽  
Shuo Shi
Keyword(s):  
Frequency Domain ◽  
Euclidean Distance ◽  
Detection Method ◽  
Saliency Map ◽  
Normal Distribution Function ◽  
Salient Region Detection ◽  
Salient Region ◽  
Region Detection ◽  
Proposed Model ◽  
Salient Regions

Salient region detection is a challenge problem in computer vision, which is useful in image segmentation, region-based image retrieval, and so on. In this paper we present a multi-resolution salient region detection method in frequency domain which can highlight salient regions with well-defined boundaries of object. The original image is sub-sampled into three multi-resolution layers, and for each layer the luminance and color salient features are extracted in frequency domain. Then, the significant values are calculated by using invariant laws of Euclidean distance in Lab space and the normal distribution function is used to specify the salient map in each layer in order to remove noise and enhance the correlation among the vicinity pixels. The final saliency map is obtained by normalizing and merging the multi-resolution salient maps. Experimental evaluation depicts the promising results from the proposed model by outperforming the state-of-art frequency-tuned model.

scholarly journals Graph-Based Salient Region Detection through Linear Neighborhoods

2016 ◽  
Vol 2016 ◽  
pp. 1-11
Author(s):  
Lijuan Xu ◽  
Fan Wang ◽  
Yan Yang ◽  
Xiaopeng Hu ◽  
Yuanyuan Sun
Keyword(s):  
Weight Function ◽  
Linear Combination ◽  
Detection Method ◽  
Saliency Detection ◽  
Detection Methods ◽  
Salient Region Detection ◽  
Salient Region ◽  
Region Detection ◽  
Graph Labelling ◽  
Gaussian Weight

Pairwise neighboring relationships estimated by Gaussian weight function have been extensively adopted in the graph-based salient region detection methods recently. However, the learning of the parameters remains a problem as nonoptimal models will affect the detection results significantly. To tackle this challenge, we first apply the adjacent information provided by all neighbors of each node to construct the undirected weight graph, based on the assumption that every node can be optimally reconstructed by a linear combination of its neighbors. Then, the saliency detection is modeled as the process of graph labelling by learning from partially selected seeds (labeled data) in the graph. The promising experimental results presented on some datasets demonstrate the effectiveness and reliability of our proposed graph-based saliency detection method through linear neighborhoods.

A Novel Salient Region Detection Method Based on Hierarchical Spatial Information

2017 ◽  
Vol 26 (2) ◽  
pp. 319-324
Author(s):  
Shuo Liu ◽  
Wenrui Ding ◽  
Hongguang Li ◽  
Yingting Li
Keyword(s):  
Spatial Information ◽  
Detection Method ◽  
Salient Region Detection ◽  
Salient Region ◽  
Region Detection

Efficient 3D mesh salient region detection using local homogeneity measure

2018 ◽  
Vol 12 (9) ◽  
pp. 1663-1672 ◽  
Author(s):  
Abdul Rahman El Sayed ◽  
Abdallah El Chakik ◽  
Hassan Alabboud ◽  
Adnan Yassine
Keyword(s):  
Salient Region Detection ◽  
3D Mesh ◽  
Local Homogeneity ◽  
Salient Region ◽  
Region Detection

Exploiting Surroundedness and Superpixel cues for salient region detection

2020 ◽  
Vol 79 (15-16) ◽  
pp. 10935-10951
Author(s):  
Yifeng Jiang ◽  
Shan Chang ◽  
Enxing Zheng ◽  
Linna Hu ◽  
Ranran Liu
Keyword(s):  
Salient Region Detection ◽  
Salient Region ◽  
Region Detection
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