Domain Adaptive Semantic Diffusion for Large Scale Context-Based Video Annotation

Learning to cope with domain change has been known as a challenging problem in many real-world applications. This paper proposes a novel and efficient approach, named domain adaptive semantic diffusion (DASD), to exploit semantic context while considering the domain-shift-ofcontext for large scale video concept annotation. Starting with a large set of concept detectors, the proposed DASD refines the initial annotation results using graph diffusion technique, which preserves the consistency and smoothness of the annotation over a semantic graph. Different from the existing graph learning methods which capture relations among data samples, the semantic graph treats concepts as nodes and the concept affinities as the weights of edges. Particularly, the DASD approach is capable of simultaneously improving the annotation results and adapting the concept affinities to new test data. The adaptation provides a means to handle domain change between training and test data, which...

• Type: Conference
• Year: 2009
• Where: ICCV
• Authors: Yu-Gang Jiang, Jun Wang, Shih-Fu Chang, Chong-Wah Ngo
• Keywords: Computer Vision, ICCV 2009, Semantic Diffusion, Video Annotation