Model Recommendation for Action Recognition and Other Applications

Pyry Matikainen
doctoral dissertation, tech. report CMU-RI-TR-12-36, Robotics Institute, Carnegie Mellon University, December, 2012


Download
  • Adobe portable document format (pdf) (10MB)
Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.

Abstract
The typical approach to learning based vision has been that for each individual application, classifiers or detectors are learned anew from annotated training data for each specific task. However, the classifiers trained in this way tend to be brittle and highly specialized to the datasets from which they are derived, making them difficult to transfer between tasks. While multi-task learning and domain adaption techniques address some of these problems on a theoretical level, from a practical standpoint they are just as complicated and labor-intensive as the simpler learning techniques they supplant. However, suppose that these specialized classifiers had simply been collected into a library: while it is unlikely that any specific classifier would generalize well to a new dataset, there may exist some classifier in the library that is tuned to the same conditions as the new task. This thesis addresses the fundamental question of how to efficiently select a good classifier from such a library. Specifically, this thesis demonstrates that collaborative filtering techniques (such as employed by recommender systems like Netflix and Amazon.com) can be used to recommend models appropriate for a specific target task. These recommendations are made by trying, or rating, a small subset of models on the target task, and then using that small set of ratings along with the ratings of the models on other tasks to predict the ratings of the unevaluated models on the target task. This process, which we term ``model recommendation'', is applied to action recognition and other vision and robotics applications, and the subtle differences between model recommendation and typical recommender systems are used to derive novel algorithms and extensions to the core recommendation concept.

Keywords
computer vision, action recognition, collaborative filtering, recommender systems

Notes

Text Reference
Pyry Matikainen, "Model Recommendation for Action Recognition and Other Applications," doctoral dissertation, tech. report CMU-RI-TR-12-36, Robotics Institute, Carnegie Mellon University, December, 2012

BibTeX Reference
@phdthesis{Matikainen_2012_7387,
   author = "Pyry Matikainen",
   title = "Model Recommendation for Action Recognition and Other Applications",
   booktitle = "",
   school = "Robotics Institute, Carnegie Mellon University",
   month = "December",
   year = "2012",
   number= "CMU-RI-TR-12-36",
   address= "Pittsburgh, PA",
}