Salient Region Detection Using Diffusion Process on a Two-Layer Sparse Graph

2017 ◽  
Vol 26 (12) ◽  
pp. 5882-5894 ◽  
Author(s):  
Li Zhou ◽  
Zhaohui Yang ◽  
Zongtan Zhou ◽  
Dewen Hu
Keyword(s):  
Diffusion Process ◽  
Sparse Graph ◽  
Salient Region Detection ◽  
Salient Region ◽  
Region Detection

scholarly journals Salient Region Detection Using Diffusion Process with Nonlocal Connections

2018 ◽  
Vol 8 (12) ◽  
pp. 2526 ◽  
Author(s):  
Huiyuan Luo ◽  
Guangliang Han ◽  
Peixun Liu ◽  
Yanfeng Wu
Keyword(s):  
Saliency Detection ◽  
Gaussian Model ◽  
Single Layer ◽  
Saliency Map ◽  
Detection Methods ◽  
Sparse Graph ◽  
Salient Region Detection ◽  
Salient Region ◽  
Region Detection ◽  
Benchmark Datasets

Diffusion-based salient region detection methods have gained great popularity. In most diffusion-based methods, the saliency values are ranked on 2-layer neighborhood graph by connecting each node to its neighboring nodes and the nodes sharing common boundaries with its neighboring nodes. However, only considering the local relevance between neighbors, the salient region may be heterogeneous and even wrongly suppressed, especially when the features of salient object are diverse. In order to address the issue, we present an effective saliency detection method using diffusing process on the graph with nonlocal connections. First, a saliency-biased Gaussian model is used to refine the saliency map based on the compactness cue, and then, the saliency information of compactness is diffused on 2-layer sparse graph with nonlocal connections. Second, we obtain the contrast of each superpixel by restricting the reference region to the background. Similarly, a saliency-biased Gaussian refinement model is generated and the saliency information based on the uniqueness cue is propagated on the 2-layer sparse graph. We linearly integrate the initial saliency maps based on the compactness and uniqueness cues due to the complementarity to each other. Finally, to obtain a highlighted and homogeneous saliency map, a single-layer updating and multi-layer integrating scheme is presented. Comprehensive experiments on four benchmark datasets demonstrate that the proposed method performs better in terms of various evaluation metrics.

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

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.

Adaptive compressed sensing of color images based on salient region detection

2019 ◽  
Vol 79 (21-22) ◽  
pp. 14777-14791
Author(s):  
Zheng Zhang ◽  
Hongbo Bi ◽  
Xiaoxue Kong ◽  
Ning Li ◽  
Di Lu
Keyword(s):  
Compressed Sensing ◽  
Color Images ◽  
Salient Region Detection ◽  
Salient Region ◽  
Region Detection
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