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Enhancing Visual Phrase Retrieval: Vocabulary Optimization and Feature Reduction

This study presents results from using Bag of Visual Phrases (BoVP) with 5 million features and a vocabulary of 1,000 words on 5,000 images. Results show an improvement over Bag of Words (BoW) with a 1% difference. The research suggests increasing the vocabulary to 10,000 and reducing the number of features for better performance. The study also addresses the limitations of the current vocabulary size and the need to discriminate between interesting and non-interesting areas in images. The analysis focuses on Radial Transaction configuration, rotation invariance, and scalability concerns, highlighting the importance of mining and sorting transactions. Further steps involve code improvements for Visual Phrases retrieval.

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Enhancing Visual Phrase Retrieval: Vocabulary Optimization and Feature Reduction

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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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