1. Unsupervised Object Segmentation with a Hybrid Graph Model (HGM) Reporter: 鄭綱 (6/14)

2. Outline  Introduction  HGM for Two-Class Clustering  HGM-based single-class object segmentation approach  Experiments on Single-Class Segmentation  Extension to Multiclass Clustering

3. Introduction  Supervised learning: classification is seen as supervised learning from examples.  Supervision: The data (observations, measurements, etc.) are labeled with pre- defined classes. It is like that a “teacher” gives the classes.  Test data are classified into these classes too.  Unsupervised learning (clustering)  Class labels of the data are unknown  Given a set of data, the task is to establish the existence of classes or clusters in the data

4. Introduction  Using the hybrid graph model (HGM), we can achieve accurate object segmentation by integrating both top-down and bottom-up information. (simultaneous)  Top-down information: local shape prior.  Bottom-up information: color/ texture…

5. HGM for Two-Class Clustering  Classify n samples (superpixels) into 2 classes: foreground and background  Score vector : the “probability” to each sample belonging to foreground.  Conditional Dependence Matrix P:  Homogeneous Association Matrix A:

6. HGM for Two-Class Clustering  Mathematic modeling From directed subgraph: (ideally: ) From undirected subgraph:  Combine them together:

7. HGM for Two-Class Clustering  Choose a threshold “t” such that samples can be classified to and  Use “normalized cut” to determine the threshold:

8. HGM-based single-class object segmentation approach  Local shape priors = visual words + spatial distance  Want to find matrix P and A.  Define matrix A:

9. HGM-based single-class object segmentation approach  Let be the n superpixels and be the m object parts. These two matrices record the overlap between superpixels & object parts. where

10. Experiments on Single-Class Segmentation  Evaluation method: F-Measure. It performs well even the object is very small.

11. Left: nHGM Middle: N-Cut Right: HGM

13. Extension to Multiclass Clustering It records the information of samples being in the same cluster.

14. Left: N-cut Right: HGM

15. Contributions & Limitations  Contributions:  Unsupervised: without human interaction, it can handle a large number of classes of objects.  It can achieve recognition.  Combine top-down & bottom-up information: it performs good segmentation result.  Limitations:  Maybe it performs worse than supervised segmentation in strong light variation.

16. References  Guangcan Liu, Zhouchen Lin, Yong Yu, and Xiaoou Tang. “Unsupervised Object Segmentation with a Hybrid Graph Model (HGM),” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol32. 2010  J. Shi and J. Malik, “Normalized Cuts and Image Segmentation,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol.22, no.8, pp. 888-905, Aug. 2000.