Selecting features for object detection using an AdaBoost-compatible evaluation function

  • Authors:

  • Luka Fürst;Sanja Fidler;Aleš Leonardis

  • Affiliations:

  • University of Ljubljana, Faculty of Computer and Information Science, Visual Cognitive Systems Laboratory, Traška 25, SI-1001 Ljubljana, Slovenia;University of Ljubljana, Faculty of Computer and Information Science, Visual Cognitive Systems Laboratory, Traška 25, SI-1001 Ljubljana, Slovenia;University of Ljubljana, Faculty of Computer and Information Science, Visual Cognitive Systems Laboratory, Traška 25, SI-1001 Ljubljana, Slovenia

  • Venue:
  • Pattern Recognition Letters
  • Year:
  • 2008

Quantified Score

Hi-index 0.10

Visualization

Abstract

This paper addresses the problem of selecting features in a visual object detection setup where a detection algorithm is applied to an input image represented by a set of features. The set of features to be employed in the test stage is prepared in two training-stage steps. In the first step, a feature extraction algorithm produces a (possibly large) initial set of features. In the second step, on which this paper focuses, the initial set is reduced using a selection procedure. The proposed selection procedure is based on a novel evaluation function that measures the utility of individual features for a certain detection task. Owing to its design, the evaluation function can be seamlessly embedded into an AdaBoost selection framework. The developed selection procedure is integrated with state-of-the-art feature extraction and object detection methods. The presented system was tested on five challenging detection setups. In three of them, a fairly high detection accuracy was effected by as few as six features selected out of several hundred initial candidates.