Abstract
This paper proposes a soft voting based bag-of-features (BoF) model considering relative distance of the feature vectors to the nearest-neighbor codeword. The proposed method is more efficient than the kernel distance based soft voting method, which requires brute force parameter optimization. The proposed algorithm is applied to human attribute analysis using top-view images and conventional object classification. The experimental results for the human attribute analysis have demonstrated 100% accuracy for both gender classification and bag possession status classification. It has also been demonstrated that discriminative ability is comparable to that of the fine-tuned kernel distance based soft voting method.
