1 / 14

Association Rule Mining on Multi-Media Data

Association Rule Mining on Multi-Media Data. Auto Annotation on Images Bhavika Patel Hau San Si Tou Juveria Kanodia Muhammad Ahmad. Auto Annotation on Images. This project is on performing Association Rule Mining on Multi-relational, Multimedia Data, particularly pictures and text.

jvarela
Download Presentation

Association Rule Mining on Multi-Media Data

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. Association Rule Mining on Multi-Media Data Auto Annotation on Images Bhavika Patel Hau San Si Tou Juveria Kanodia Muhammad Ahmad

  2. Auto Annotation on Images • This project is on performing Association Rule Mining on Multi-relational, Multimedia Data, particularly pictures and text. • Corpus: a group of 798 picturesof different kinds such as art, landscape … with descriptions • Generate association rules on image data (the RGBY values), and on text data separately. Propose an algorithm to link these two different domains together. • Goal: return words that will describe a given unknown picture

  3. Offline Processing

  4. Multi-Arm Program

  5. Association Rules on Text

  6. Association Rules on Text

  7. Association Rules on Image Data

  8. Association Rules on Image Data

  9. # of text association rules generated from different combination of min supp & conf

  10. # of image association rules generated from different combination of min supp & conf

  11. Single pass rebuild • Specify common key • Rebuild the tables based on the common key • Use Apriori • EXAMPLE: Table 1: purchase(customer,item,amount) item(customer,item_id) Table 2 purchase_total(customer,items) Query: Customers who buy a lot of stuff what do they usually but? purchase_total(X,items) return item(X,item_id)

  12. Conclusion • So we have a partial solution multimedia ARM problem, however there many things that can be done further, to improve upon it. • Need to find a way to restrict the number of keywords that we get. • Need to find an easier method than the present lookup method, as too many files are involved. • Need for an efficient data structure to do the above point. • Alternative Schemes

  13. The End Please visit our project’s website at http://www.cs.rit.edu/~p759-06c to find detailed information.

  14. Questions?

More Related