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 Features Extraction Model Database Hypothesis Generation Hypothesis Verification Matching

Images  Total: images, - 21 different objects  Training Data Set positive image pairs negative image pairs - 14 different objects  Testing Data Set positive image pairs 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 MalikFaces in the NewsDataset

C.) Hypothesis Verification  Sammon mapping for toy cars

Results 1.) Toy Cars2.) Ferencz 3.) Faces4.) 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);