1. Florian Schroff, Antonio Criminisi & Andrew Zisserman ICCV 2007 Harvesting Image Databases from the Web

2. Outline Goal: retrieve class specific images from the web images are ranked using a multi-modal approach: text & meta data from the web pages visual features Algorithm 1.enter object keyword (e.g. penguin) 2. retrieve set of images using Google web search 3. filter to remove drawings & abstract images 4. rank images using meta-data from web pages 5. train SVM on visual features using (4) as noisy training data 6. final ranking using trained SVM

3. Example: Penguin 1.Enter “penguin” 2.Retrieve images from web pages returned by Google web search on penguin 522 in-class, 1771 non-class 3. Remove drawings & abstract images 391 in-class, 784 non-class

4. Example: Penguin continued 4. rank images using naïve Bayes metadata ranker 5. Train SVM on visual features using ranked images as noisy training data 6. Final re-ranking using trained SVM

5. Details of Abstract Filter

6. Details of Meta-data Re-rank Filter

7. Example: Penguin continued

8. More examples classes – cars, elephants

9. More examples classes – watches, zebras