Relative-distance-based soft voting for feature representation and its application to human attribute analysis

This paper proposes a soft voting based bag-of-features (BoF) model considering relative distance of the feature vectors to the nearest-neighbor codeword. Whereas state-of-the-art kernel distance based soft voting methods require brute force parameter optimization, which is time consuming, the proposed method does not require any optimization. The proposed algorithm was applied to human attribute analysis using top-view images. The experimental results have demonstrated 100% of accuracy for both gender classification and baggage possession classification. It has also been demonstrated that discriminative ability is comparable to that of the fine-tuned codeword uncertainty (UNC) model.