1 / 9

Florian Schroff, Antonio Criminisi & Andrew Zisserman ICCV 2007

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

tamika
Download Presentation

Florian Schroff, Antonio Criminisi & Andrew Zisserman ICCV 2007

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Florian Schroff, Antonio Criminisi & Andrew ZissermanICCV 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 enter object keyword (e.g. penguin) retrieve set of images using Google web search filter to remove drawings & abstract images rank images using meta-data from web pages train SVM on visual features using (4) as noisy training data final ranking using trained SVM

  3. Example: Penguin • Enter “penguin” • 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

More Related