TY - JFULL AU - Lei Zhang and Tao Wang and Xiantong Zhen PY - 2012/12/ TI - Evaluation of Classifiers Based On I2C Distance for Action Recognition T2 - International Journal of Computer and Information Engineering SP - 1450 EP - 1457 VL - 6 SN - 1307-6892 UR - https://publications.waset.org/pdf/12474 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 71, 2012 N2 - Naive Bayes Nearest Neighbor (NBNN) and its variants, i,e., local NBNN and the NBNN kernels, are local feature-based classifiers that have achieved impressive performance in image classification. By exploiting instance-to-class (I2C) distances (instance means image/video in image/video classification), they avoid quantization errors of local image descriptors in the bag of words (BoW) model. However, the performances of NBNN, local NBNN and the NBNN kernels have not been validated on video analysis. In this paper, we introduce these three classifiers into human action recognition and conduct comprehensive experiments on the benchmark KTH and the realistic HMDB datasets. The results shows that those I2C based classifiers consistently outperform the SVM classifier with the BoW model. ER -