Unsupervised Salience Learning for Person Re-identification

CVPR2013 Poster

Outline

Introduction Method Experiments Conclusions

Introduction

Human eyes can recognize person identities based on some small salient regions.

Introduction

Person re-identification handles pedestrian matching and ranking across non-overlapping camera views.

viewpoint change and pose variation cause uncontrolled misalignment between images.

Introduction

Motivations : 1. We can recognize persons across

camera views from their local distinctive regions

2. Human salience 3. Distinct patches are considered as

salient only when they are matched and distinct in both camera views

Method

Dense Correspondence Unsupervised Salience Learning Matching for Re-identification

Dense Correspondence

Features: dense color histogram + dense SIFT

Adjacency constrained search: simple patch matching

Adjacency constrained search

Search set :

Adjacency constrained search

Adjacency Searching:

Unsupervised Salience Learning

two methods for learning human salience:

K-Nearest Neighbor Salience (KNN) One-Class SVM Salience (OCSVM)

Unsupervised Salience Learning

Definition: Salient regions are discriminative in making a person standing out from their companions, and reliable in finding the same person across camera views.

Assumption: fewer than half of the persons in a reference set share similar appearance if a region is salient. Hence, we set k = Nr/2. Nr is the number of images in reference set.

Unsupervised Salience Learning

K-Nearest Neighbor Salience

K-Nearest Neighbor Salience

Unsupervised Salience Learning

One-Class SVM Salience

Matching for Re-identification

Bi-directional Weighted Matching

Complementary Comination

Matching for Re-identification

Experiments

Dataset : VIPeR Dataset ETHZ Dataset

Experiments

Experiments

Experiments

Experiments

Experiments

Conclusions

1. An unsupervised framework to extract distinctive features for person re-identification.

2. Patch matching is utilized with adjacency constraint for handling the misalignment

problem caused by viewpoint change and pose variation.

3. Human salience is learned in an unsupervised way.