1. Saliency detection
Donghun Yeo
CV Lab.

2. Contents

3. Definition of Saliency Detection
Locate important and interesting regions or objects in an image.
Generally the output is saliency value for each pixel.
input image GT
Result of [Mai et al. 2013]
input image
GT of saliency value
[Mai et al. 2013] Saliency Aggregation: A Data-driven Approach, CVPR 2013

4. Hierarchical Saliency Detection
Qiong Yan, Li Xu, Jianping Shi, Jiaya Jia, The Chinese University of Hong Kong
Published in CVPR 2013

5. General Approach 1. Segment a given image.
2. Compute saliency of each segment based on color contrasts, shape of segments and etc.

6. Segmentation scale problem
Saliency Detection depends on the segmentation scale.
The goal of this paper is fusing the results from different level of segmentation to achieve better saliency detection.
Small scale
Large scale
Easy to detect small-structures
Easy to detect large-structures

7. Overview of Hierarchical Saliency Detection

8. 1. Image layers Three layers.
Merge smallest region and its nearest neighbor in terms of average CIELUV color distance until the smallest region scale is larger than a specific threshold (3, 17, 33).
Maintain the tree structure of merging regions for each layer.

9. 2.Single-Layer Saliency Cues
Local Contrast
Location Heuristic
Saliency Cue
: color of region
: number of pixels in =
: Euclidean distance between two region centers
: Set of pixel coordinates

10. 2.Single-Layer Saliency Cues

11. Hierarchical Inference
Minimize the following energy function
Saliency value for each region must similar to the initial saliency.
And merging components and the merging region must have similar saliency.
: Saliency variable for region in layer

12. Experiments
MSRA-1000

13. Experiments
MSRA-1000

14. Experiments
Complex Scene Saliency Dataset

15. Experiments
Complex Scene Saliency Dataset

16. vs. Single layer
Complex Scene Saliency Dataset

17. Saliency Aggregation: A Data-driven Approach
Long Mai, Yuzhen Niu, Feng Liu
Published in CVPR 2013

18. Aggregate the results of saliency detection

19. Model Saliency values from different methods for each pixel p
Model the aggregated saliency by using binary conditional random field(CRF).
Binary random label =1 for salient pixel and 0 for non-salient pixel.
Grid-shaped CRF (8-neighbors)
Find Y which maximizes the conditional probability as follow

20. Conditional Probability
Conditional Probability of labels Y on the features X
Design the reliability of individual saliency detection methods.

21. Conditional Probability
Conditional Probability of labels Y on the features X
If difference between saliency values of neighbor pixels are large, then the label of the pixels must be different
Neighbor pixels which have similar color have same label.
constant

22. Image-Dependent Saliency Aggregation
The performance of individual methods varies over images.
We train the parameters for each test image by using k-nearest neighbors of the given test image.
Similarity is computed by GIST descriptor.

23. Experiments

24. Experiments

25. Experiments