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In the area of multimedia processing, a number of studies have been devoted to narrowing the gap between multimedia content and human sense. In fact, multimedia understanding is a difficult and challenging task even using machine-learning techniques. To deal with this challenge, in this paper, we propose an innovative method that employs data mining techniques and content-based paradigm to conceptualize videos. Mainly, our proposed method puts the focus on: (I) Construction of prediction models, namely speech-association model ModelSass and visual-statistical model ModelCRM, and (2) Fusion of prediction models to annotate unknown videos automatically. Without additional manual cost, discovered speech-association patterns can show the implicit relationships among the sequential images. On the other hand, visual features can atone for the inadequacy of speech-association patterns. Empirical evaluations reveal that our approach makes, on the average, the promising results than other methods for annotating videos.

Effective Video Annotation by Mining Visual Features and Speech Features