Unsupervised Object Segmentation with a Hybrid Graph Model (HGM)

Reporter: 鄭綱 (6/14)

Outline

Introduction HGM for Two-Class Clustering HGM-based single-class object

segmentation approach Experiments on Single-Class Segmentation Extension to Multiclass Clustering

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

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…

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:

[ ]ij n np P

[ ]ij n na A

1{ ,..., }nV v v

T1 2( , ,..., )n

C C

HGM for Two-Class Clustering

Mathematic modeling From directed subgraph: (ideally: ) From undirected subgraph: Combine them together:

HGM for Two-Class Clustering

Choose a threshold “t” such that samples can be classified to and

Use “normalized cut” to determine the threshold:

:C

:C

HGM-based single-class object segmentation approach

Local shape priors = visual words + spatial distance Want to find matrix P and A. Define matrix A:

HGM-based single-class object segmentation approach

Let be the n superpixels and be the m object parts.

1 2 P K F K

These two matrices record the overlap between superpixels & object parts.

where

Experiments on Single-Class Segmentation

Evaluation method: F-Measure. It performs well even the object is very

small.

Left: nHGMMiddle: N-CutRight: HGM

Extension to Multiclass Clustering

It records the information of samples being in the same cluster.

Left: N-cutRight: HGM

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.

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.