Recognizing Human Interactions Using Group Feature Relevance in Multinomial Kernel Logistic Regression

We propose a supervised approach incorporating groupfeature sparsity in multi-class kernel logistic regression(GFR-MKLR). The need for group sparsity arises inseveral practical situations where a subset of a set offactors can explain a predicted variable and each factorconsists of a group of variables. We apply our approachfor predicting human interactions based on bodyparts motion (e.g., hands, legs, head, etc.) where imagefeatures are organised in groups corresponding to bodyparts. Our approach, leads to sparse models by assigningweights to groups of features having the highest discriminationbetween different types of interactions. Experimentsconducted on the UT-Interaction dataset havedemonstrated the performance of our method with regardto stat-of-art methods.