In this paper we describe a system to classify TV programs into predefined categories based on the analysis of their video contents. This is very useful in intelligent display and storage systems that can select channels and record or skip contents according to the consumer's preference. Distinguishable patterns exist in different categories of TV programs in terms of human faces and super-imposed text. By applying face and text tracking to a number of training video segments, including commercials, news, sitcoms, and soaps, we have identified patterns within each category of TV programs in a predefined feature space that reflects the face and text characteristics of the video. A given video segment is projected to the feature space and compared against the distribution of known categories of TV programs. Domain-knowledge is used to help the classification. Encouraging results have been achieved so far in our initial experiments.
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