Title

Discovering Motion Primitives For Unsupervised Grouping And One-Shot Learning Of Human Actions, Gestures, And Expressions

Keywords

action recognition; action representation; facial expressions; gestures; Hidden Markov model; histogram of motion primitives; Human actions; motion patterns; motion primitives; motion primitives strings; one-shot learning; unsupervised clustering

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. © 1979-2012 IEEE.

Publication Date

5-29-2013

Publication Title

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volume

35

Issue

7

Number of Pages

1635-1648

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/TPAMI.2012.253

Socpus ID

84878144777 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84878144777

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