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Florian Schroff, Antonio Criminisi & Andrew ZissermanICCV 2007
Harvesting Image Databases from the Web
OutlineGoal: 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
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
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
Details of Abstract Filter
Details of Meta-data Re-rank Filter
Example: Penguin continued
More examples classes – cars, elephants
More examples classes – watches, zebras