Association rule mining on multi media data
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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.

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Association Rule Mining on Multi-Media Data

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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

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


Offline processing

Offline Processing


Multi arm program

Multi-Arm Program


Association rules on text

Association Rules on Text


Association rules on text1

Association Rules on Text


Association rules on image data

Association Rules on Image Data


Association rules on image data1

Association Rules on Image Data


Of text association rules generated from different combination of min supp conf

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


Of image association rules generated from different combination of min supp conf

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


Single pass rebuild

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)


Conclusion

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


The end

The End

Please visit our project’s website at http://www.cs.rit.edu/~p759-06c

to find detailed information.


Questions

Questions?


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