On supervision and statistical learning for semantic multimedia analysis
Media analysis for video indexing is witnessing an increasing influence of statistical techniques. Examples of these techniques include the use of generative models as well as discriminant techniques for video structuring, classification, summarization, indexing, and retrieval. There is increasing emphasis on reducing the amount of supervision and user interaction needed to construct and utilize the semantic models. This paper highlights the statistical learning techniques in semantic multimedia indexing and retrieval. In particular the gamut of techniques from supervised to unsupervised systems will be demonstrated.
factor graphs sum product algorithm active learning hidden markov models dynamic bayesian networks support vector machines