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Section: Scientific Foundations

Stochastic Models for Multimodal Analysis

Describing multimedia documents, i.e. , documents that contain several modalities (e.g. text, images, sound) requires to take all these modalities into account since they can contain complementary pieces of information. The problem is that the various modalities are only weakly synchronized, they do not have the same rate and combining the information that can be extracted from them is not obvious. Of course, we would like to find generic ways to combine these pieces of information. Stochastic models appear as a well dedicated tool for such combinations, especially for image and sound information.

Markov models are composed of a set of states, of transition probabilities between these states and of emission probabilities that provide the probability to emit a given symbol at a given state. Such models allow to generate sequences. Starting from an initial state, they iteratively emit a symbol and then switch in a subsequent state according to the respective probability distributions. These models can be used in an indirect way. Given a sequence of symbols (called observations), hidden Markov models (HMM, [62] ) aim at finding the best sequence of states that can explain this sequence. The Viterbi algorithm provides an optimal solution to this problem.

For such HMM, the structure and probability distributions need to be a priori determined. They can be fixed manually (this is the case for the structure: number of states and their topology), or estimated from example data (this is often the case for the probability distributions). Given a document, such an HMM can be used to retrieve its structure from the features that can be extracted. As a matter of fact, these models allow an audiovisual analysis of the videos, the symbols being composed of a video and an audio component.

Two of the main drawbacks of the HMM is that they can only emit a unique symbol per state, and that they imply that the duration in a given state follows an exponential distribution. Such drawbacks can be circumvented by segment models [61] . These models are an extension of HMM were each state can emit several symbols and contains a duration model that governs the number of symbols emitted (or observed) for this state. Such a scheme allows us to process features at different rates.

Bayesian networks are an even more general model family. Static Bayesian networks [49] are composed of a set of random variables linked by edges indicating their conditional dependency. Such models allow us to learn from example data the distributions and links between the variables. A key point is that both the network structure and the distributions of the variables can be learned. As such, these networks are difficult to use in the case of temporal phenomena.

Dynamic Bayesian [58] networks are a generalization of the previous models. Such networks are composed of an elementary network that is replicated at each time stamp. Duration variable can be added in order to provide some flexibility on the time processing, like it was the case with segment models.

While HMM and segment models are well suited for dense segmentation of video streams, Bayesian networks offer better capabilities for sparse event detection. Defining a trash state that corresponds to non event segments is a well known problem is speech recognition: computing the observation probabilities in such a state is very difficult.


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