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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. - PowerPoint PPT Presentation
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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);