Importance of Feature Locations in Bag-of-Words Image Classification

The impact of image feature locations in the bag-of-words model for object classification is examined. It is demonstrated that a simple variance-based method works well and offers advantages over several other methods. In essence, the feature locations are selected intelligently, decreasing the redundancy and cost sometimes associated with feature extraction on dense grids. Classification results on two databases are presented, using a support vector machine classifier.