Instance-specific and Class-consistent Cues

• We aim to resolve the difficulties of action recognition arising from the large intra-class variations. These unfavorable variations make it infeasible to represent one action instance by other ones of the same action. We hence propose to extract both instance-specific and class-consistent features to facilitate action recognition.
• Credits
  • Instance-specific features: Self-similarities among frames of an action sequence. Multivariate linear prediction (MLP) is adopted to aggregate all the causalities among frames.
  • Class-consistent features: Characteristics shared by instances of the same action. Support vector machines (SVMs) are used to discover these features based on the bag-of-words model.
  • We propose a generative formulation to integrate the two complementary types of features, and boost the performance.

Core Techniques

• We view actions as multivariate time signals. For signal processing in our approach, there are several essential techniques:
  • Wide-Sense Stationary Process
  • Linear Prediction
  • Support Vector Machine
• We propose a generative model. The main idea is to consider the static and dynamic information of a multivariate time signal separately. This is based on the assumption that these two information are independent for action recognition.
  • Instance-specific cue via MLP
  • Class-consistent cue via SVM
  • Linear fusion
    　

Action Recognition using Instance-specific and Class-consistent Cues

Chin-An Lin, Yen-Yu Lin, Hong-Yuan Mark Liao, and Shyh-Kang Jeng

IEEE International Conference on Image Processing (ICIP), September 2012

Paper