Probabilistic Graphical Models for Human Interaction Analysis

The objective of this thesis is to develop probabilistic graphical models for analyzing human interaction in meetings based on multimodel cues. We use meeting as a study case of human interactions since research shows that high complexity information is mostly exchanged through face-to-face interactions. Modeling human interaction provides several challenging research issues for the machine learning community. In meetings, each participant is a multimodal data stream. Modeling human interaction involves simultaneous recording and analysis of multiple multimodal streams. These streams may be asynchronous, have different frame rates, exhibit different stationarity properties, and carry complementary (or correlated) information. In this thesis, we developed three probabilistic graphical models for human interaction analysis. The proposed models use the ``probabilistic graphical model'' formalism, a formalism that exploits the conjoined capabilities of graph theory and probability theory to build complex models out of simpler pieces. We first introduce the multi-layer framework, in which the first layer models typical individual activity from low-level audio-visual features, and the second layer models the interactions. The two layers are linked by a set of posterior probability-based features. Next, we describe the team-player influence model, which learns the influence of interacting Markov chains within a team. The team-player influence model has a two-level structure: individual-level and group-level. Individual level models actions of each player, and the group-level models actions of the team as a whole. The influence of each player on the team is jointly learned with the rest of the model parameters in a principled manner using the Expectation-Maximization (EM) algorithm. Finally, we describe the semi-supervised adapted HMMs for unusual event detection. Unusual events are characterized by a number of features (rarity, unexpectedness, and relevance) that limit the application of traditional supervised model-based approaches. We propose a semi-supervised adapted Hidden Markov Model (HMM) framework, in which usual event models are first learned from a large amount of (commonly available) training data, while unusual event models are learned by Bayesian adaptation in an unsupervised manner.

Dong Zhang

École Polytechnique Fédérale de Lausanne2006

Probabilistic graphical models for human interaction analysis

Dong Zhang

The objective of this thesis is to develop probabilistic graphical models for analyzing human interaction in meetings based on multimodel cues. We use meeting as a study case of human interactions since research shows that high complexity information is mostly exchanged through face-to-face interactions. Modeling human interaction provides several challenging research issues for the machine learning community. In meetings, the feature sequence extracted from each participant is multimodal. Modeling human interaction involves simultaneous recording and analysis of multiple multimodal streams. These streams may be asynchronous, have different frame rates, exhibit different stationarity properties, and carry complementary (or correlated) information. In this thesis, we developed three probabilistic graphical models for human interaction analysis. The proposed models use the "probabilistic graphical model" formalism, a formalism that exploits the conjoined capabilities of graph theory and probability theory to build complex models out of simpler pieces. We first introduce the multi-layer framework, in which the first layer models typical individual activity from low-level audio-visual features, and the second layer models the interactions. The two layers are linked by a set of posterior probability-based features. Next, we describe the team-player influence model, which learns the influence of interacting Markov chains within a team. The team-player influence model has a two-level structure: individual-level and group-level. Individual level models actions of each player, and the group-level models actions of the team as a whole. The influence of each player on the team is jointly learned with the rest of the model parameters in a principled manner using the Expectation-Maximization (EM) algorithm. Finally, we describe the semi-supervised adapted HMMs for unusual event detection. Unusual events are characterized by a number of features (rarity, unexpectedness, and relevance) that limit the application of traditional supervised model-based approaches. We propose a semi-supervised adapted Hidden Markov Model (HMM) framework, in which usual event models are first learned from a large amount of (commonly available) training data, while unusual event models are learned by Bayesian adaptation in an unsupervised manner.

EPFL2006

Prior knowledge in Kernel methods

Alexei Pozdnoukhov

Machine Learning is a modern and actively developing field of computer science, devoted to extracting and estimating dependencies from empirical data. It combines such fields as statistics, optimization theory and artificial intelligence. In practical tasks, the general aim of Machine Learning is to construct algorithms able to generalize and predict in previously unseen situations based on some set of examples. Given some finite information, Machine Learning provides ways to exract knowledge, describe, explain and predict from data. Kernel Methods are one of the most successful branches of Machine Learning. They allow applying linear algorithms with well-founded properties such as generalization ability, to non-linear real-life problems. Support Vector Machine is a well-known example of a kernel method, which has found a wide range of applications in data analysis nowadays. In many practical applications, some additional prior knowledge is often available. This can be the knowledge about the data domain, invariant transformations, inner geometrical structures in data, some properties of the underlying process, etc. If used smartly, this information can provide significant improvement to any data processing algorithm. Thus, it is important to develop methods for incorporating prior knowledge into data-dependent models. The main objective of this thesis is to investigate approaches towards learning with kernel methods using prior knowledge. Invariant learning with kernel methods is considered in more details. In the first part of the thesis, kernels are developed which incorporate prior knowledge on invariant transformations. They apply when the desired transformation produce an object around every example, assuming that all points in the given object share the same class. Different types of objects, including hard geometrical objects and distributions are considered. These kernels were then applied for images classification with Support Vector Machines. Next, algorithms which specifically include prior knowledge are considered. An algorithm which linearly classifies distributions by their domain was developed. It is constructed such that it allows to apply kernels to solve non-linear tasks. Thus, it combines the discriminative power of support vector machines and the well-developed framework of generative models. It can be applied to a number of real-life tasks which include data represented as distributions. In the last part of the thesis, the use of unlabelled data as a source of prior knowledge is considered. The technique of modelling the unlabelled data with a graph is taken as a baseline from semi-supervised manifold learning. For classification problems, we use this apporach for building graph models of invariant manifolds. For regression problems, we use unlabelled data to take into account the inner geometry of the input space. To conclude, in this thesis we developed a number of approaches for incorporating some prior knowledge into kernel methods. We proposed invariant kernels for existing algorithms, developed new algorithms and adapted a technique taken from semi-supervised learning for invariant learning. In all these cases, links with related state-of-the-art approaches were investigated. Several illustrative experiments were carried out on real data on optical character recognition, face image classification, brain-computer interfaces, and a number of benchmark and synthetic datasets.

EPFL2006

Prior knowledge in Kernel methods

Alexei Pozdnoukhov

EPFL2006

Probabilistic Graphical Models for Human Interaction Analysis

Dong Zhang

École Polytechnique Fédérale de Lausanne2006

Probabilistic graphical models for human interaction analysis

Dong Zhang

The objective of this thesis is to develop probabilistic graphical models for analyzing human interaction in meetings based on multimodel cues. We use meeting as a study case of human interactions since research shows that high complexity information is mostly exchanged through face-to-face interactions. Modeling human interaction provides several challenging research issues for the machine learning community. In meetings, the feature sequence extracted from each participant is multimodal. Modeling human interaction involves simultaneous recording and analysis of multiple multimodal streams. These streams may be asynchronous, have different frame rates, exhibit different stationarity properties, and carry complementary (or correlated) information. In this thesis, we developed three probabilistic graphical models for human interaction analysis. The proposed models use the "probabilistic graphical model" formalism, a formalism that exploits the conjoined capabilities of graph theory and probability theory to build complex models out of simpler pieces. We first introduce the multi-layer framework, in which the first layer models typical individual activity from low-level audio-visual features, and the second layer models the interactions. The two layers are linked by a set of posterior probability-based features. Next, we describe the team-player influence model, which learns the influence of interacting Markov chains within a team. The team-player influence model has a two-level structure: individual-level and group-level. Individual level models actions of each player, and the group-level models actions of the team as a whole. The influence of each player on the team is jointly learned with the rest of the model parameters in a principled manner using the Expectation-Maximization (EM) algorithm. Finally, we describe the semi-supervised adapted HMMs for unusual event detection. Unusual events are characterized by a number of features (rarity, unexpectedness, and relevance) that limit the application of traditional supervised model-based approaches. We propose a semi-supervised adapted Hidden Markov Model (HMM) framework, in which usual event models are first learned from a large amount of (commonly available) training data, while unusual event models are learned by Bayesian adaptation in an unsupervised manner.

EPFL2006