Multi‐group–multi‐class domain adaptation for event recognition

Abstract

In this study, the authors propose a multi‐group–multi‐class domain adaptation framework to recognise events in consumer videos by leveraging a large number of web videos. The authors’ framework is extended from multi‐class support vector machine by adding a novel data‐dependent regulariser, which can force the event classifier to become consistent in consumer videos. To obtain web videos, they search them using several event‐related keywords and refer the videos returned by one keyword search as a group. They also leverage a video representation which is the average of convolutional neural networks features of the video frames for better performance. Comprehensive experiments on the two real‐world consumer video datasets demonstrate the effectiveness of their method for event recognition in consumer videos.

Keywords

• multigroup-multi-class domain adaptation
• event recognition
• Web videos
• multiclass SVM
• data-dependent regulariser
• video representation