Workshop on Machine Learning Techniques for Processing Multimedia Content

Abstract

Machine Learning (ML) techniques are used in situations where data is available in electronic format and ML algorithms can ?add value? by analysing this data. This is the situation with the processing of multimedia content. The ?added value? from ML can take a number of forms: ? by providing insight into the domain from which the data is drawn, ? by improving the performance of another process that is manipulating the data, ? by organising the data in some way or ? by helping to interpret multimedia content to make it more understandable. This potential for ML to add value in processing of multimedia content has made this one of the most popular application areas for ML research. Multimedia content has some characteristics that place specific demands on ML. The data is typically of very high dimension and dimension reduction is often required. The normal distinction between supervised and unsupervised techniques doesn?t always apply; it is often the case that only some of the data is labeled or the user may assist in labeling the data during processing. Typically the ML process is preceded by a feature extraction stage and the success of the ML stage will often depend on the feature extraction. This workshop on Machine Learning Techniques for Processing Multimedia Content has been organized because of these special issues that arise with multimedia data. We have papers describing applications in image processing, video analysis and music classification. The research described in these papers has drawn on a wide range of ML techniques. It is hoped that this workshop will help identify important research directions for Machine Learning that will help in the processing of multimedia content.