International Journal of Computer Vision

, Volume 77, Issue 1, pp 3-24

First online:

Learning to Locate Informative Features for Visual Identification

  • Andras FerenczAffiliated withMobileye Vision Technologies Email author 
  • , Erik G. Learned-MillerAffiliated withComputer Science, UMass Amherst
  • , Jitendra MalikAffiliated withComputer Science, U.C. Berkeley

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Object identification is a specialized type of recognition in which the category (e.g. cars) is known and the goal is to recognize an object’s exact identity (e.g. Bob’s BMW). Two special challenges characterize object identification. First, inter-object variation is often small (many cars look alike) and may be dwarfed by illumination or pose changes. Second, there may be many different instances of the category but few or just one positive “training” examples per object instance. Because variation among object instances may be small, a solution must locate possibly subtle object-specific salient features, like a door handle, while avoiding distracting ones such as specular highlights. With just one training example per object instance, however, standard modeling and feature selection techniques cannot be used. We describe an on-line algorithm that takes one image from a known category and builds an efficient “same” versus “different” classification cascade by predicting the most discriminative features for that object instance. Our method not only estimates the saliency and scoring function for each candidate feature, but also models the dependency between features, building an ordered sequence of discriminative features specific to the given image. Learned stopping thresholds make the identifier very efficient. To make this possible, category-specific characteristics are learned automatically in an off-line training procedure from labeled image pairs of the category. Our method, using the same algorithm for both cars and faces, outperforms a wide variety of other methods.


Object recognition Object identification Parametric models Interclass transfer Learning from new examples One-shot learning