Incorporating geometry information with weak classifiers for improved generic visual categorization
In this paper we improve the performance of a generic visual categorizer based on the ''bag of keypatches'' approach using geometric information. More precisely, we consider a large number of simple geometrical relationships between interest points based on the scale, orientation or closeness. Each relationship leads to a weak classifier. The boosting approach is used to select from this multitude of classifiers (several millions in our case) and to combine them effectively with the original classifier. Results are shown on a new challenging 10 class dataset.