By Oliver Aberth
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Algebraic Video Environment), the first algebra for querying video. The challenges behind audio and video indexing and retrieval are discussed in Section V. It starts with a survey of the state of the art in the area of audio content indexing and retrieval by Liu and Wang (Chapter 20). In Chapter 21, Ortega-Binderberger and Mehrotra discuss the important concept of relevance feedback and some of the techniques that have been successfully applied to multimedia search and retrieval. In Chapter 22, Santini proposes a novel approach to structuring and organizing video data using experience units and discusses some of its philosophical implications.
Recent work in semantic video indexing includes Naphade et al. [13,40,41]. 2 FUSION MODELS Audio-Visual analysis to detect the semantic concepts in videos poses a challenging problem. One main difficulty arises from the fact that the different sensors are noisy in terms of the information they contain about different Modeling Video 27 semantic concepts. For example, based on pure vision, it is hard to make out between a explosion and normal fire. Similarly, audio alone may give confusing information.
While, on the other hand, these different sources may provide complementary information, which may be essential in inference. Motivated by these difficulties, in the past few years a lot of research has gone into developing algorithms for fusing information from different modalities. ) If we assume that one can get features from the different streams on a common scale of time, the two main categories of fusion models are those that favor early integration of features versus those that favor late integration.
Computable analysis by Oliver Aberth