Type-2 Fuzzy Topic Models for Human Action Recognition

Following the "bag-of-words" representation for video sequences, we propose novel type-2 fuzzy topic models (T2 FTM) to recognize human actions. In traditional topicmodels (TM) for visual recognition, each video sequence is modeled as a "document" composed of spatial-temporal interest points called visual words. Topic models automatically assign a "topic" label to explain the action category of each word so that each video sequence becomes a mixture of action topics for recognition. Our T2 FTM differs from previous TM in that it uses type-2 fuzzy sets to encode the higher order uncertainty of each topic. We can use the primary membership function (MF) to measure the degree of uncertainty that a document or a visual word belongs to a specific action topic, and use the secondary MF to evaluate the fuzziness of the primary MF itself. In this paper, we implement two T2 FTM: 1) interval T2 FTM with all secondary grades equal one, and 2) vertical-slice T2 FTM with unequal secondary grades based on our prior knowledge. To estimate parameters in T2 FTM, we derive the efficient message-passing algorithms. Experiments on KTH, Weizmann, UCF, and Hollywood2 human action datasets demonstrate that T2 FTM performs better than other state-of-the-art topic models for human action recognition.