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Visual Phrases - PowerPoint PPT Presentation


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Visual Phrases. Ivette Carreras Haroon Idrees. Selecting features to build phrases. Experiment in landmark 1, 132 Images Features with high scale – top 50% Resulted in very short phrases: length 1-3 only All features regardless of scale Resulted in longer phrases: length 1-7.

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visual phrases

Visual Phrases

Ivette Carreras

HaroonIdrees

selecting features to build phrases
Selecting features to build phrases
  • Experiment in landmark 1, 132 Images
    • Features with high scale – top 50%
      • Resulted in very short phrases: length 1-3 only
    • All features regardless of scale
      • Resulted in longer phrases: length 1-7
steps to follow
Steps to follow
  • Go through every feature of every image and build the transactions
  • Mine the resulting file
  • Read and sort the found phrases by their frequency
    • Select a percentage for the top frequency
    • Currently using 20%
steps to follow1
Steps to follow
  • Go through all the features of every image and find a match for a given phrase
    • Count the phrases in an image and build the Bag of Visual Phrases
current status
Current Status
  • Obtained transactions for all images
  • Mined them with different minimum supports
min supt 500 frequencies
Min_supt 500 Frequencies
  • Currently working on building the BoVP for these transactions
next steps
Next Steps
  • Finish building the Bag of Visual Phrases for all images
  • Find mAP for BoVP– mean average precision
  • Compare results from BoW and BoVP
    • Our 1K BoW – 20% mAP