1. Object Segmentation Based on Multiple Features Fusion and Conditional Random Field
CASIA_IGIT
National Laboratory of Pattern Recognition(NLPR)
Institute of Automation, Chinese Academy of Sciences(CASIA)
Reporter：Kun Ding（丁昆）

2. Outline
System Overview
System Characteristics 
Results and Conclusions

3. Outline
System Overview
System Characteristics 
Results and Conclusions

4. System Overview Object Segmentation Pipeline Stage 1 :
Superpixel Segmentation
Feature Extraction
SVM Classification
GrabCut
Feature
Engineering
Input Image
Superpixels
Features
Probabilistic Output
Final Results
Stage 1 :
Superpixel Classification
Stage 2 :
Pixel-based CRF Smoothing

5. System Overview Superpixel Classification Superpixel Segmentation
Graph-based image segmentation
Feature Extraction:
To be detailed in next section
SVM Classification[1]
RBF kernel with Probabilistic Output

6. System Overview Pixel-Based CRF Smoothing
Fusing several kinds of information as data term
Solving with GrabCut with only a few iterations
First
Iteration
Second
Iteration
Binarize
SVM Probabilistic Output
CRF Smoothing Output

7. Outline
System Overview
System Characteristics 
Results and Conclusions

8. System Characteristics
Superpixel Segmentation --
Efficient Graph-Based Image Segmentation[2]
Fast, property of edge-preserving
Speeding up the whole procedure
Improving the separability between foreground and background
Superpixels and their edge-preserving property

9. System Characteristics
Feature Engineering –
Superpixel-Based Multiple Features Fusion
Gradient
Dense SIFT[3][4] dictionary with Bag-of-Words description
Texture
Multi-scale LBP histogram
Color and skin
RGB histogram and HS histogram with skin detection
Density of rectangles returned from AdaBoost as probability, with manifold ranking[6] refinement
PCA
Geometrical
Position, direction and roundness
Saliency
Color spatial distribution, multi-scale local and global contrast
Probability derived from AdaBoost, with manifold ranking[6] refinement
Results of Object Detection

10. System Characteristics
Feature Engineering –
Superpixel-Based Multiple Features Fusion
Illustration of object detection
Object Detection result
Rectangle Density as Probability
Refined with Manifold Ranking

11. System Characteristics
Pixel-Based CRF Smoothing – GrabCut[7]
Modified data term
Solving by maxflow iteratively
SVM Result
Object Detection
Result
GMM Result for Foreground and Background
CRF Smoothing Output

12. Outline
System Overview
System Characteristics 
Results and Conclusions

13. Conclusion and Results Exhibition

14. Conclusion and Results Exhibition
Superpixel classification
Feature fusion works
CRF smoothing improves the results of SVM
Object parts sometimes lost
Context information is inadequate

15. Selected References
[1] C.-C. Chang and C.-J. Lin. LIBSVM: a library for support vector machines, Software available at http: // [2] Felzenszwalb P F, Huttenlocher D P. Efficient graph-based image segmentation[J]. International Journal of Computer Vision, 2004, 59(2): [3] Lowe D G. Distinctive image features from scale-invariant keypoints[J]. International journal of computer vision, 2004, 60(2): [4] Vedaldi A, Fulkerson B. VLFeat: An open and portable library of computer vision algorithms[C]//Proceedings of the international conference on Multimedia. ACM, 2010:

16. Selected References
[5] Liu T, Yuan Z, Sun J, et al. Learning to detect a salient object[J]. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 2011, 33(2): [6] Chuan Yang, Lihe Zhang, Huchuan Lu, Minghsuan Yang, Saliency Detection via Graph-Based Manifold Ranking, CVPR2013, P [7] Rother C, Kolmogorov V, Blake A. Grabcut: Interactive foreground extraction using iterated graph cuts[C]//ACM Transactions on Graphics (TOG). ACM, 2004, 23(3):

17. Thank you very much! Any questions?
CASIA_IGIT
Leader: Ying Wang （王颖）
Members: Kun Ding （丁昆）
Huxiang Gu （谷鹄翔）
Yongchao Gong （宫永超）
s: {ywang, kding, hxgu,