PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Efficient Mining of Frequent and Distinctive Feature Configurations
Till Quack, Vittorio Ferrari, Bastian Leibe and Luc Van Gool
In: International Conference on Computer Vision (ICCV'07), 14-20 Oct 2007, Rio de Janeiro, Brasil.

Abstract

We present a novel approach to automatically find spatial configurations of local features occurring frequently on instances of a given object class, and rarely on the background. The approach is based on computationally efficient data mining techniques and can find frequent configurations among tens of thousands of candidates within seconds. Based on the mined configurations we develop a method to select features which have high probability of lying on previously unseen instances of the object class. The technique is meant as an intermediate processing layer to filter the large amount of clutter features returned by lowlevel feature extraction, and hence to facilitate the tasks of higher-level processing stages such as object detection.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
Learning/Statistics & Optimisation
ID Code:3953
Deposited By:Luc Van Gool
Deposited On:25 February 2008