Florian Schroff, Antonio Criminisi & Andrew Zisserman ICCV 2007 Harvesting Image Databases from the...
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Transcript of Florian Schroff, Antonio Criminisi & Andrew Zisserman ICCV 2007 Harvesting Image Databases from the...
![Page 1: Florian Schroff, Antonio Criminisi & Andrew Zisserman ICCV 2007 Harvesting Image Databases from the Web.](https://reader030.fdocuments.in/reader030/viewer/2022032701/56649c905503460f94949498/html5/thumbnails/1.jpg)
Florian Schroff, Antonio Criminisi & Andrew ZissermanICCV 2007
Harvesting Image Databases from the Web
![Page 2: Florian Schroff, Antonio Criminisi & Andrew Zisserman ICCV 2007 Harvesting Image Databases from the Web.](https://reader030.fdocuments.in/reader030/viewer/2022032701/56649c905503460f94949498/html5/thumbnails/2.jpg)
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
![Page 3: Florian Schroff, Antonio Criminisi & Andrew Zisserman ICCV 2007 Harvesting Image Databases from the Web.](https://reader030.fdocuments.in/reader030/viewer/2022032701/56649c905503460f94949498/html5/thumbnails/3.jpg)
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
![Page 4: Florian Schroff, Antonio Criminisi & Andrew Zisserman ICCV 2007 Harvesting Image Databases from the Web.](https://reader030.fdocuments.in/reader030/viewer/2022032701/56649c905503460f94949498/html5/thumbnails/4.jpg)
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
![Page 5: Florian Schroff, Antonio Criminisi & Andrew Zisserman ICCV 2007 Harvesting Image Databases from the Web.](https://reader030.fdocuments.in/reader030/viewer/2022032701/56649c905503460f94949498/html5/thumbnails/5.jpg)
Details of Abstract Filter
![Page 6: Florian Schroff, Antonio Criminisi & Andrew Zisserman ICCV 2007 Harvesting Image Databases from the Web.](https://reader030.fdocuments.in/reader030/viewer/2022032701/56649c905503460f94949498/html5/thumbnails/6.jpg)
Details of Meta-data Re-rank Filter
![Page 7: Florian Schroff, Antonio Criminisi & Andrew Zisserman ICCV 2007 Harvesting Image Databases from the Web.](https://reader030.fdocuments.in/reader030/viewer/2022032701/56649c905503460f94949498/html5/thumbnails/7.jpg)
Example: Penguin continued
![Page 8: Florian Schroff, Antonio Criminisi & Andrew Zisserman ICCV 2007 Harvesting Image Databases from the Web.](https://reader030.fdocuments.in/reader030/viewer/2022032701/56649c905503460f94949498/html5/thumbnails/8.jpg)
More examples classes – cars, elephants
![Page 9: Florian Schroff, Antonio Criminisi & Andrew Zisserman ICCV 2007 Harvesting Image Databases from the Web.](https://reader030.fdocuments.in/reader030/viewer/2022032701/56649c905503460f94949498/html5/thumbnails/9.jpg)
More examples classes – watches, zebras