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Object Segmentation Based on Multiple Features Fusion and Conditional Random Field

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） 2013.10.17. Outline. System Overview System Characteristics

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Object Segmentation Based on Multiple Features Fusion and Conditional Random Field

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  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） 2013.10.17

  2. Outline • System Overview • System Characteristics • Results and Conclusions

  3. Outline • System Overview • System Characteristics • Results and Conclusions

  4. System Overview • Object Segmentation Pipeline Superpixel Segmentation Feature Extraction SVM Classification GrabCut Feature Engineering Features Probabilistic Output Superpixels Final Results Input Image Stage 1 : Superpixel Classification Stage 2 : Pixel-basedCRF 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 separabilitybetween foreground and background Superpixels and their edge-preserving property

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

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

  12. Outline • System Overview • System Characteristics • Results and Conclusions

  13. Conclusion and Results Exhibition • Results Exhibition

  14. Conclusion and Results Exhibition • Conclusion • 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, 2001. Software available at http: //www.csie.ntu.edu.tw/˜cjlin/libsvm. [2]Felzenszwalb P F, Huttenlocher D P. Efficient graph-based image segmentation[J]. International Journal of Computer Vision, 2004, 59(2): 167-181. [3] Lowe D G. Distinctive image features from scale-invariant keypoints[J]. International journal of computer vision, 2004, 60(2): 91-110. [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: 1469-1472.

  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): 353-367. [6]Chuan Yang, Lihe Zhang, Huchuan Lu, Minghsuan Yang, Saliency Detection via Graph-Based Manifold Ranking, CVPR2013, P3166-3173 [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): 309-314.

  17. Thank you very much!Any questions? CASIA_IGIT Leader: Ying Wang （王颖） Members: Kun Ding （丁昆） HuxiangGu（谷鹄翔） Yongchao Gong （宫永超） E-mails: {ywang,kding, hxgu, yongchao.gong}@nlpr.ia.ac.cn

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