Guest Editorial: Image Analysis and Processing Leveraging Additional Information

Many multimedia analysis and processing applications can benefit from better image understanding. However, accurate understanding of what the underlying visual content represents still remains a very challenging problem. Systems need to look beyond the pure matrix of intensity values and exploit other types of information. Actually, humans do not solve visual problems based just on the captured pixel data. Many non-visual clues and diverse types of external information are exploited, including prior knowledge, prior experience, contextual and collaborative information. Intelligent systems can often incorporate them to simplify the problem, and can also integrate other types of signals, such depth and infrarred images. Often this additional information can be critical to better address the problem or increase the quality of the result. This special issue contains 10 papers, selected from the inital 25 submissions, after two rounds of blind review. Priveleged or expert information can help to guide learning by exploiting indirect cues. In “Facial Expression Recognition through Modeling Age-related Spatial Patterns” [6], Wang et al describe a system that leverages age information, available only during training, to