Saliency Detection via Absorbing Markov Chain
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Citations
Reversion Correction and Regularized Random Walk Ranking for Saliency Detection.
Salient object detection via global and local cues
Deep Unsupervised Saliency Detection: A Multiple Noisy Labeling Perspective
GraB: Visual Saliency via Novel Graph Model and Background Priors
Deep Unsupervised Saliency Detection: A Multiple Noisy Labeling Perspective
References
A model of saliency-based visual attention for rapid scene analysis
A model of saliency-based visual attention for rapid scene analysis
A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics
Frequency-tuned salient region detection
Global contrast based salient region detection
Related Papers (5)
Frequently Asked Questions (15)
Q2. What is the effect of using the boundary nodes as absorbing nodes?
Since the boundary nodes usually contain the global characteristics of the image background, by using them as absorbing nodes, the absorbed time of each transient node can reflect its overall similarity with the background, which helps to distinguish salient nodes from background nodes.Β
Q3. Why is the proposed method performing poorly on this dataset?
Due to scrambled backgrounds and heterogeneous foregrounds most images have, and the lack of top-down prior knowledge, the overall performance of the existing bottom-up saliency detection methods is low on this dataset.Β
Q4. What is the effect of the boundary nodes on the saliency of the image background?
as the absorbed time is the expected time to all the absorbing nodes, it covers the effect of all the boundary nodes, which can alleviate the influence of particular regions and encourage the similar nodes in a local context to have the similar saliency, thereby overcoming the defects of using the equilibrium distribution [9, 14, 11, 31].Β
Q5. How can random walk be used to detect saliency?
As salient objects seldom occupy all four image boundaries [33, 5] and the background regions often have appearance connectivity with image boundaries, when the authors use the boundary nodes as absorbing nodes, the random walk starting in background nodes can easily reach the absorbing nodes.Β
Q6. What are the limitations of equilibrium distribution based saliency models?
In addition, equilibrium distribution based saliency models only highlight the boundaries of salient object while object interior still has low saliency value.Β
Q7. What is the effect of the sparse connectivity of the graph?
The sparse connectivity of the graph results that the background nodes near the image center have longer absorbed time than the similar nodes near the image boundaries.Β
Q8. What is the method used to compute precision and recall?
the authors bisegment the saliency map using every threshold in the range [0 : 0.05 : 1], and compute precision and recall at each value of the threshold to plot the precision-recall curve.Β
Q9. Why are some virtual nodes added to the graph as absorbing states?
Because the authors compute the full resolution saliency map, some virtual nodes are added to the graph as absorbing states, which is detailed in the next section.Β
Q10. How can the saliency map be suppressed?
By the update processing, the saliency of the long-range homogeneous regions near the image center can be suppressed as Figure 3 illustrates.Β
Q11. How do the authors solve the saliency detection problem?
The authors further explore the effect of the equilibrium probability in saliency detection, and exploit it to regulate the absorbed time, thereby suppressing the saliency of this kind of regions.Β
Q12. How can a Markov chain be completely specified?
Given a set of states π = {π 1, π 2, . . . , π π}, a Markov chain can be completely specified by the π Γπ transition matrix P, in which πππ is the probability of moving from state π π to state π π .Β
Q13. What is the weighting column vector for the normalized absorbed time?
In this work, the authors use the normalized recurrent time of an ergodic Markov chain, of which the transition matrix is the row normalized Q, as the weight u.Β
Q14. How do the authors reduce the saliency map?
To alleviate this problem, the authors update the saliency map by using a weighted absorbed time yw, which can be denoted as:yw = NΓ u, (10) where u is the weighting column vector.Β
Q15. What is the saliency of each transient state?
Given an input image represented as a Markov chain and some background absorbing states, the saliency of each transient state is defined as the expected number of timesbefore being absorbed into all absorbing nodes by Eq 2.Β