Confidence-assisted classification result refinement for object recognition featuring TopN-Exemplar-SVM

Toshihiko Yamasaki, Tsuhan Chen

Abstract: This paper proposes a cascaded classifier framework for better image recognition. The proposed method is based on the confidence values given by the classifiers. By using our proposed topN-Exemplar SVM in the second stage and comparing the confidence values with those from the first stage, the classification results with less confidence are successfully updated. The validity of our algorithm has been demonstrated by the experiments using three standard image datasets.