Type-2 fuzzy labeled latent Dirichlet allocation for human action categorization

We may represent human actions as a bag of spatiotemporal visual words extracted from input video sequences. For human action categorization, labeled LDA (L-LDA) is an extension of latent Dirichlet allocation (LDA) by providing action class labels to each video. To handle parameter uncertainty in L-LDA, this paper further extends L-LDA within the type-2 fuzzy set (T2 FS) framework, referred to as T2 L-LDA. Because the membership function (MF) of T2 FS is three-dimensional, we can use the primary MF to measures the degree of uncertainty that a visual word belongs to a specified human action category, and use the secondary MF to evaluate the fuzziness of the primary MF itself. We also develop the T2 fuzzy belief propagation (T2F BP) algorithm for approximate inference and parameter estimation based on T2 FS operations. On the KTH human motion data set, our results show that T2 L-LDA is able to enhance the overall performance in human action categorization.