Saliency Detection via Absorbing Markov Chain With Learnt Transition Probability

2018 ◽  
Vol 27 (2) ◽  
pp. 987-998 ◽  
Author(s):  
Lihe Zhang ◽  
Jianwu Ai ◽  
Bowen Jiang ◽  
Huchuan Lu ◽  
Xiukui Li
Keyword(s):  
Markov Chain ◽  
Transition Probability ◽  
Saliency Detection ◽  
Absorbing Markov Chain

Region saliency detection via multi-feature on absorbing Markov chain

2015 ◽  
Vol 32 (3) ◽  
pp. 275-287 ◽  
Author(s):  
Wenjie Zhang ◽  
Qingyu Xiong ◽  
Weiren Shi ◽  
Shuhan Chen
Keyword(s):  
Markov Chain ◽  
Saliency Detection ◽  
Absorbing Markov Chain

scholarly journals Markov Chain and Adaboost Image Saliency Detection Algorithm Based on Conditional Random Field

2021 ◽  
Vol 15 ◽  
pp. 762-773
Author(s):  
Biao Lu ◽  
Nannan Liang ◽  
Chengfang Tan ◽  
Zhenggao Pan
Keyword(s):  
Markov Chain ◽  
Random Field ◽  
Markov Chain Model ◽  
Conditional Random Field ◽  
Transition Probability ◽  
Saliency Detection ◽  
Saliency Map ◽  
Chain Model ◽  
Image Saliency ◽  
Saliency Region

The traditional salient object detection algorithms are used to apply the underlying features and prior knowledge of the images. Based on conditional random field Markov chain and Adaboost image saliency detection technology, a saliency detection method is proposed to effectively reduce the error caused by the target approaching the edge, which mainly includes the use of absorption Markov chain model to generate the initial saliency map. In this model, the transition probability of each node is defined by the difference of color and texture between each super pixel, and the absorption time of the transition node is calculated as the significant value of each super pixel. A strong classifier optimization model based on Adaboost iterative algorithm is designed.The initial saliency map is processed by the classifier to obtain an optimized saliency map, which highlights the global contrast. In order to extract the saliency region of the final saliency map, a method using conditional random field is designed to segment and extract the saliency region. The results show that the saliency area detected by this method is prominent, the overall contour is clear and has high resolution. At the same time, this method has better performance in accuracy recall curve and histogram.

Mesh saliency detection via double absorbing Markov chain in feature space

2015 ◽  
Vol 32 (9) ◽  
pp. 1121-1132 ◽  
Author(s):  
Xiuping Liu ◽  
Pingping Tao ◽  
Junjie Cao ◽  
He Chen ◽  
Changqing Zou
Keyword(s):  
Markov Chain ◽  
Saliency Detection ◽  
Feature Space ◽  
Mesh Saliency ◽  
Absorbing Markov Chain

Saliency Detection via Absorbing Markov Chain

2013 ◽  
Author(s):  
Bowen Jiang ◽  
Lihe Zhang ◽  
Huchuan Lu ◽  
Chuan Yang ◽  
Ming-Hsuan Yang
Keyword(s):  
Markov Chain ◽  
Saliency Detection ◽  
Absorbing Markov Chain

scholarly journals Saliency Detection with Bilateral Absorbing Markov Chain Guided by Depth Information

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 838
Author(s):  
Jiajia Wu ◽  
Guangliang Han ◽  
Peixun Liu ◽  
Hang Yang ◽  
Huiyuan Luo ◽  
...  
Keyword(s):  
Markov Chain ◽  
Saliency Detection ◽  
Color Space ◽  
Saliency Map ◽  
Depth Information ◽  
Detection Model ◽  
Absorbing Markov Chain ◽  
Detection Failure ◽  
Benchmark Datasets ◽  
High Level

The effectiveness of depth information in saliency detection has been fully proved. However, it is still worth exploring how to utilize the depth information more efficiently. Erroneous depth information may cause detection failure, while non-salient objects may be closer to the camera which also leads to erroneously emphasis on non-salient regions. Moreover, most of the existing RGB-D saliency detection models have poor robustness when the salient object touches the image boundaries. To mitigate these problems, we propose a multi-stage saliency detection model with the bilateral absorbing Markov chain guided by depth information. The proposed model progressively extracts the saliency cues with three level (low-, mid-, and high-level) stages. First, we generate low-level saliency cues by explicitly combining color and depth information. Then, we design a bilateral absorbing Markov chain to calculate mid-level saliency maps. In mid-level, to suppress boundary touch problem, we present the background seed screening mechanism (BSSM) for improving the construction of the two-layer sparse graph and better selecting background-based absorbing nodes. Furthermore, the cross-modal multi-graph learning model (CMLM) is designed to fully explore the intrinsic complementary relationship between color and depth information. Finally, to obtain a more highlighted and homogeneous saliency map in high-level, we structure a depth-guided optimization module which combines cellular automata and suppression-enhancement function pair. This optimization module refines the saliency map in color space and depth space, respectively. Comprehensive experiments on three challenging benchmark datasets demonstrate the effectiveness of our proposed method both qualitatively and quantitatively.

On the age distribution of a Markov chain

1978 ◽  
Vol 15 (1) ◽  
pp. 65-77 ◽  
Author(s):  
Anthony G. Pakes
Keyword(s):  
Markov Chain ◽  
Random Walk ◽  
Limit Theorems ◽  
Age Distribution ◽  
Weak Limit ◽  
Absorbing State ◽  
Absorbing Markov Chain ◽  
Continuous Random Walk ◽  
A Chain ◽  
Watson Process

This paper develops the notion of the limiting age of an absorbing Markov chain, conditional on the present state. Chains with a single absorbing state {0} are considered and with such a chain can be associated a return chain, obtained by restarting the original chain at a fixed state after each absorption. The limiting age, A(j), is the weak limit of the time given Xn = j (n → ∞).A criterion for the existence of this limit is given and this is shown to be fulfilled in the case of the return chains constructed from the Galton–Watson process and the left-continuous random walk. Limit theorems for A (J) (J → ∞) are given for these examples.

Sign in / Sign up

Export Citation Format

Share Document