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Visual Phrases and Data Mining

Visual Phrases and Data Mining. Ivette Carreras Haroon Idrees. Results from BoVP. Using 5M features for 5K images Vocabulary size 1K Measurement : mean Average Precision ( mAP ) Results from BoW 18% Results from BoVP 1%. Differnces. Vocabulary size is too small for 5M features

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Visual Phrases and Data Mining

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  1. Visual Phrases and Data Mining Ivette Carreras HaroonIdrees

  2. Results from BoVP • Using 5M features for 5K images • Vocabulary size 1K • Measurement : mean Average Precision (mAP) • Results from BoW 18% • Results from BoVP 1%

  3. Differnces • Vocabulary size is too small for 5M features • 5M 50K words • Every phrase is used in retrieval (length 2:6) • Same weight for every length • Transactions are created for every word in each images • Do not discriminate between interesting and not interesting areas in the image • Van Gool’s paper focuses only on certain areas

  4. Radial Transaction configuration • Rotation invariant • Not sensible to scale • Unless many new other words (not part of the phrase) appear • Transactions are already mined and sorted B A A C B D

  5. Next steps • Increase the vocabulary to 10K and decrease the number of features • Fix the Visual Phrases code

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