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Laboratoire d'InfoRmatique en Image et Systèmes d'information

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Laboratoire d'InfoRmatique en Image et Systèmes d'information
UMR 5205 CNRS / INSA Lyon / Université Claude Bernard Lyon 1 / Université Lumière Lyon 2 / École Centrale de Lyon
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Does Scarlett smile more than her French lover ? Constraints and regularization in visual metric learning

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Matthieu Cord, Professor at UPMC-Sorbonne Universities, LIP6, IUF

What
  • Séminaire mensuel
  • Séminaire
When Feb 03, 2015
from 10:30 AM to 12:00 PM
Where Amphi Emilie du Châtelet, bibliothèque Marie Curie, INSA de Lyon
Contact Name
Contact Phone +33 (0)4.72.43.14.34
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Metric learning is useful in many Computer Vision applications, such as image classification, retrieval, face verification, or person re-identification. Supervised metric learning has been deeply investigated to compare feature vectors. I will focus in this talk on Mahalanobis-like distance metric learning that essentially infer a linear transformation of the data space. The key components of this learning problem are data constraints and regularization.

I first present classic metric learning approaches based on image pairs or triplets constraints. I discuss specific extensions focusing on image attributes (e.g. smiling) ranked by classes (e.g. a given celebrity) or inferred from richer relationships, e.g., a hierarchy of classes.  In a second part, I discuss  the regularization term for this type of learning scheme. In particular, I present classic methods to minimize the rank of the learned model in a Mahalanobis-like matrix learning. I then introduce interesting regularizer to explicitly control the matrix rank. Several results on image datasets and Webpage screenshot comparison will be discussed.

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