Title :
Graph-based multiple instance learning for action recognition
Author :
Zixin Guo ; Yang Yi
Author_Institution :
Sch. of Inf. Sci. & Technol., Sun Yat-sen Univ., Guangzhou, China
Abstract :
This paper presents a novel framework for recognizing realistic actions captured from unconstrained environments. We describe an action as a collection of space-time activity parts, which are adaptively extracted by clustering foreground trajectories. Each video part is associated with a Bag-of-Features (BoF) histogram, yielding our bag-of-histograms representation for video. We formulate our action classification problem within the graph-based Multiple Instance Learning (MIL) framework, in which each activity part is cast as an instance and a graphical model is incorporated with MIL to leverage the interaction information among the instances. We evaluate our method on two challenging action datasets and demonstrate significant improvements over the state-of-the-art BoF baseline algorithm.
Keywords :
graph theory; learning (artificial intelligence); planning (artificial intelligence); video signal processing; BoF baseline algorithm; BoF histogram; MIL framework; action classification problem; action recognition; bag-of-features histogram; bag-of-histograms representation; clustering foreground trajectories; graph-based multiple instance learning; graphical model; space-time activity parts; unconstrained environments; Action Recognition; Bag-of-Feature; Dense Trajectory; Multiple Instance Learning;
Conference_Titel :
Image Processing (ICIP), 2013 20th IEEE International Conference on
Conference_Location :
Melbourne, VIC
DOI :
10.1109/ICIP.2013.6738772