Bayesian methods for multimedia signal processing
Ali Taylan Cemgil · 2007
In the last years, there have been a significant growth of multimedia information processing applications that employ ideas from statistical machine learning and probabilistic modeling. In this paradigm, multimedia data (music, audio, video, images, text, ...) are viewed as realizations from highly structured stochastic processes. Once a model is constructed, several interesting problems such as transcription, coding, classification, restoration, tracking, source separation or resynthesis etc. can be formulated as Bayesian inference problems. In this context, graphical models provide a "language" to construct models for quantification of prior knowledge. Unknown parameters in this specification are estimated by probabilistic inference. Often, however, the problem size poses an important challenge and in order to render the approach feasible, specialized inference methods need to be tailored to improve the computational speed and efficiency.