1. Multi-Class Object Recognition Using Shared SIFT Features
Siddharth Batra

2. ? Problem Statement Object recognition for a “class” of objects + + =
using a dictionary of “shared” features.

3. Approach – Create a Dictionary of Features
Set of Shared Descriptors
Find locations to share in all images
Gradient & Orientation Maps
Variance filtering
Shared Descriptor
For each set of keypoint to be shared
Compute keypoints for each image

4. Approach – Train & Run a Classifier
Compute Feature Vectors based on Shared Descriptors
Train a Naïve Bayes classifier
Evaluate multiple overlapping window using classifier
Threshold based on probability
Non- maximal suppression

5. Results - Mugs
On a set of 32 images with 64 mugs, the algorithm gets 88% recall & 90% precision

6. Results - Keyboards
On a set of 60 images with 64 keyboards, the algorithm gets 100% recall & 100% precision