Current research trends in computer vision have tended towards achieving the goal of recognizing human action, due to the potential utility of such recognition in various applications. Among many potential approaches, an approach involving Gaussian Mixture Model (GMM) supervectors with a Support Vector Machine (SVM) and a nonlinear GMM KL kernel has been proven to yield improved performance for recognizing human activities. In this study, based on tensor analysis, we develop and exploit an extended class of action features that we refer to as gradient-flow tensor divergence. The proposed method has shown a best recognition rate of 96.3% for a KTH dataset, and reduced processing time.

Acoustic Event Classification (AEC) poses difficult technical challenges as a result of the complexity in capturing and processing sound data. Of the various applicable approaches, Support Vector Machine (SVM) with Gaussian Mixture Model (GMM) supervectors has been proven to obtain better solutions for such problems. In this paper, based on the multiple kernel selection model, we introduce two non-linear kernels, which are derived from the linear kernels of GMM Kullback-Leibler divergence (GMM KL) and GMM-UBM mean interval (GUMI). The proposed method improved the AEC model's accuracy from 85.58% to 90.94% within the domain of home AEC.