Learning Visual Similarity Measures for Comparing Never Seen Objects

By: Eric Nowark, Frederic Juric

Presented by: Khoa Tran

Outline 1.) Purpose 2.) Methodology 3.) Results

Purpose

Object Recognition

Train Images

Test Images

Methodology Preview

A.) Corresponding patch pair

B.) Quantizing patch pair

C.) Patch pair similarity measure

Object Recognition 1.) Images 2.) Feature

Extraction 3.) Model Database 4.) Matching

a.) Hypothesis Generation

b.) Hypothesis Verification

Images

FeaturesExtraction

Model Database

Hypothesis Generation

Hypothesis Verification

Matching

Images Total: - 225 images,

- 21 different objects

Training Data Set - 1185 positive image pairs

- 7330 negative image pairs

- 14 different objects

Testing Data Set - 1044 positive image pairs

- 6337 negative image pairs

- 7 different objects

Feature Extraction Patches

Normalized Cross Correlation

SIFT Descriptors Matrix representation

Model Database Extremely

Randomized Binary Decision Tree SIFT Descriptors Geometric

Information

Information Gain

Model Database – SIFT Descriptors

Model Database

Hypothesis Generation – Similar Measure Similar Measure Support Vector Machine

Hypothesis Generation

Ferencz and Malik Faces in the NewsDataset Dataset

C.) Hypothesis Verification

Sammon mapping for toy cars

Results

1.) Toy Cars 2.) Ferencz

3.) Faces 4.) Coil 100

Reference Eric Nowak and Fredric Jurie; "Learning Visual

Similarity Measures for Comparing Never Seen Objects” ;Computer Vision and Pattern Recognition 2007 (CVPR'07);