title: Discovering motion primitives for unsupervised grouping and one-shot learning of human actions, gestures, and expressions.
abstract: This paper proposes a novel representation of articulated human actions and gestures and facial expressions. The main goals of the proposed approach are: 1) to enable recognition using very few examples, i.e., one or k-shot learning, and 2) meaningful organization of unlabeled datasets by unsupervised clustering. Our proposed representation is obtained by automatically discovering high-level subactions or motion primitives, by hierarchical clustering of observed optical flow in four-dimensional, spatial, and motion flow space. The completely unsupervised proposed method, in contrast to state-of-the-art representations like bag of video words, provides a meaningful representation conducive to visual interpretation and textual labeling. Each primitive action depicts an atomic subaction, like directional motion of limb or torso, and is represented by a mixture of four-dimensional Gaussian distributions. For one--shot and k-shot learning, the sequence of primitive labels discovered in a test video are labeled using KL divergence, and can then be represented as a string and matched against similar strings of training videos. The same sequence can also be collapsed into a histogram of primitives or be used to learn a Hidden Markov model to represent classes. We have performed extensive experiments on recognition by one and k-shot learning as well as unsupervised action clustering on six human actions and gesture datasets, a composite dataset, and a database of facial expressions. These experiments confirm the validity and discriminative nature of the proposed representation.
authors.affiliation: Department of Electrical Engineering and Computer Science, University of Central Florida, Orlando, USA. yyang@eecs.ucf.edu
authors.forename: Yang
authors.initials: Y
authors.lastname: Yang
authors.forename: Imran
authors.initials: I
authors.lastname: Saleemi
authors.forename: Mubarak
authors.initials: M
authors.lastname: Shah
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D016208:Databases, Factual; D005149:Facial Expression; D005868:Gestures; D006802:Human Activities / Q000145:classification*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008390:Markov Chains; D009068:Movement; D010363:Pattern Recognition, Automated / Q000379:methods*; D014741:Video Recording