Abstract: 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.
Abstract: We studied the evolution of elliptic heavy SF6
gas cylinder surrounded by air when accelerated by a planar
Mach 1.25 shock. A multiple dynamics imaging technology has
been used to obtain one image of the experimental initial
conditions and five images of the time evolution of elliptic
cylinder. We compared the width and height of the circular and
two kinds of elliptic gas cylinders, and analyzed the vortex
strength of the elliptic ones. Simulations are in very good
agreement with the experiments, but due to the different initial
gas cylinder shapes, a certain difference of the initial density
peak and distribution exists between the circular and elliptic
gas cylinders, and the latter initial state is more sensitive and
more inenarrable.