Exploiting Feature and Class Relationships in Video Categorization with Regularized Deep Neural Networks

Understand

In this paper, we study the challenging problem of categorizing videos according to high-level semantics such as the existence of a particular human action or a complex event.

• Although extensive efforts have been devoted in recent years, most existing works combined multiple video features using simple fusion strategies and neglected the utilization of inter-class semantic relationships.
• This paper proposes a novel unified framework that jointly exploits the feature relationships and the class relationships for improved categorization performance.
• Specifically, these two types of relationships are estimated and utilized by rigorously imposing regularizations in the learning process of a deep neural network (DNN).