Upload
quang
View
48
Download
0
Tags:
Embed Size (px)
DESCRIPTION
User Adaptive Image Ranking for Search Engines. Maryam Mahdaviani Nando de Freitas Laboratory for Computational Intelligence University of British Columbia. Word Polysemy is a common problem in IR system. Screen shot of apple/red apple/red apple fruit Screen shot of tiger. - PowerPoint PPT Presentation
Citation preview
User Adaptive Image Ranking for Search Engines
Maryam Mahdaviani Nando de FreitasLaboratory for Computational Intelligence
University of British Columbia
• Screen shot of apple/red apple/red apple fruit
• Screen shot of tiger
Image Retrieval systems mainly use linguistic
features (e.g. words) and not visual cues
Word Polysemy is a common problem in
IR system
How do we do it? Instance Preference Learning by Gaussian Processes
• We want to learn a better ranking from m pair-wise relations: for
• We use the standard hierarchical Bayes probit model [Hebrich et al, NIPS 06; Wei Chu et al, ICML 05]
mk ,...,1kk uv
How do we do it? Instance Preference Learning by Gaussian Processes
• It then follows that :
• The posterior can be easily computed either using MCMC, Laplace’s method, mean field or Expectation Propagation.
Can also do Active Preference Learning
• The system prompts user with intelligent questions to increase the confidence in ranking
• The user can stop questioning once she is annoyed
• The system re-ranks the images based on the preferences
• We calculate for each unlabeled pair; pick the maximum and query the user accordingly [Wei Chu et al, NIPS 05]
Conclusion and Future Directions
• We applied state-of-the-art preference learning algorithm for image ranking
• In future we should work on:
Improving the HCI
Improving the vision
Conducting using study
Expand the idea to other search Learning from many sources