Mixture Models for Multidimensional Positive Data Clustering with Applications to Image Categorization and Retrieval

Model-based approaches have become important tools to model data and infer knowledge. Such approaches are often used for clustering and object recognition which are crucial steps in many applications, including but not limited to, recommendation systems, search engines, cyber security, surveillance and object tracking. Many of these applications have the urgent need to reduce the semantic gap of data representation between the system level and the human being understandable level. Indeed, the low level features extracted to represent a given object can be confusing to machines which cannot differentiate between very similar objects trivially distinguishable by human beings (e.g. apple vs tomato). Such a semantic gap between the system and the user perception for data, makes the modeling process hard to be designed basing on the features space only. Moreover those models should be flexible and updatable when new data are introduced to the system. Thus, apart from estimating the model parameters, the system should be somehow informed how new data should be perceived according to some criteria in order to establish model updates. In this thesis we propose a methodology for data representation using a hierarchical mixture model basing on the inverted Dirichlet and the generalized inverted Dirichlet distributions. The proposed approach allows to model a given object class by a set of components deduced by the system and grouped according to labeled training data representing the human level semantic. We propose an update strategy to the system components that takes into account adjustable metrics representing users perception. We also consider the "page zero" problem in image retrieval systems when a given user does not possess adequate tools and semantics to express what he/she is looking for, while he/she can visually identify it. We propose a statistical framework that enables users to start a search process and interact with the system in order to find their target "mental image". Finally we propose to improve our models by using a variational Bayesian inference to learn generalized inverted Dirichlet mixtures with features selection. The merit of our approaches is evaluated using extensive simulations and real life applications

Bayesian learning of concept ontology for automatic image annotation

Ph.DDOCTOR OF PHILOSOPH

Advanced Probabilistic Models for Clustering and Projection

Probabilistic modeling for data mining and machine learning problems is a fundamental research area. The general approach is to assume a generative model underlying the observed data, and estimate model parameters via likelihood maximization. It has the deep probability theory as the mathematical background, and enjoys a large amount of methods from statistical learning, sampling theory and Bayesian statistics. In this thesis we study several advanced probabilistic models for data clustering and feature projection, which are the two important unsupervised learning problems. The goal of clustering is to group similar data points together to uncover the data clusters. While numerous methods exist for various clustering tasks, one important question still remains, i.e., how to automatically determine the number of clusters. The first part of the thesis answers this question from a mixture modeling perspective. A finite mixture model is first introduced for clustering, in which each mixture component is assumed to be an exponential family distribution for generality. The model is then extended to an infinite mixture model, and its strong connection to Dirichlet process (DP) is uncovered which is a non-parametric Bayesian framework. A variational Bayesian algorithm called VBDMA is derived from this new insight to learn the number of clusters automatically, and empirical studies on some 2D data sets and an image data set verify the effectiveness of this algorithm. In feature projection, we are interested in dimensionality reduction and aim to find a low-dimensional feature representation for the data. We first review the well-known principal component analysis (PCA) and its probabilistic interpretation (PPCA), and then generalize PPCA to a novel probabilistic model which is able to handle non-linear projection known as kernel PCA. An expectation-maximization (EM) algorithm is derived for kernel PCA such that it is fast and applicable to large data sets. Then we propose a novel supervised projection method called MORP, which can take the output information into account in a supervised learning context. Empirical studies on various data sets show much better results compared to unsupervised projection and other supervised projection methods. At the end we generalize MORP probabilistically to propose SPPCA for supervised projection, and we can also naturally extend the model to S2PPCA which is a semi-supervised projection method. This allows us to incorporate both the label information and the unlabeled data into the projection process. In the third part of the thesis, we introduce a unified probabilistic model which can handle data clustering and feature projection jointly. The model can be viewed as a clustering model with projected features, and a projection model with structured documents. A variational Bayesian learning algorithm can be derived, and it turns out to iterate the clustering operations and projection operations until convergence. Superior performance can be obtained for both clustering and projection

Basic research planning in mathematical pattern recognition and image analysis

Fundamental problems encountered while attempting to develop automated techniques for applications of remote sensing are discussed under the following categories: (1) geometric and radiometric preprocessing; (2) spatial, spectral, temporal, syntactic, and ancillary digital image representation; (3) image partitioning, proportion estimation, and error models in object scene interference; (4) parallel processing and image data structures; and (5) continuing studies in polarization; computer architectures and parallel processing; and the applicability of "expert systems" to interactive analysis

World Modeling for Intelligent Autonomous Systems

The functioning of intelligent autonomous systems requires constant situation awareness and cognition analysis. Thus, it needs a memory structure that contains a description of the surrounding environment (world model) and serves as a central information hub. This book presents a row of theoretical and experimental results in the field of world modeling. This includes areas of dynamic and prior knowledge modeling, information fusion, management and qualitative/quantitative information analysis

Approche probabiliste hybride pour la recherche d'images par le contenu avec pondération des caractéristiques

Durant la dernière décennie, des quantités énormes de documents visuels (images et vidéos) sont produites chaque jour par les scientifiques, les journalistes, les amateurs, etc. Cette quantité a vite démontré la limite des systèmes de recherche d'images par mots clés, d'où la naissance du paradigme qu'on nomme Système de Recherche d'Images par le Contenu, en anglais Content-Based Image Retrieval (CBIR). Ces systèmes visent à localiser les images similaires à une requête constituée d'une ou plusieurs images, à l'aide des caractéristiques visuelles telles que la couleur, la forme et la texture. Ces caractéristiques sont dites de bas-niveau car elles ne reflètent pas la sémantique de l'image. En d'autres termes deux images sémantiquement différentes peuvent produire des caractéristiques bas-niveau similaires. Un des principaux défis de cette nouvelle vision des systèmes est l'organisation de la collection d'images pour avoir un temps de recherche acceptable. Pour faire face à ce défi, les techniques développées pour l'indexation des bases de données textuelles telles que les arbres sont massivement utilisées. Ces arbres ne sont pas adaptés aux données de grandes dimensions, comme c'est le cas des caractéristiques de bas-niveau des images. Dans ce mémoire, nous nous intéressons à ce défi. Nous introduisons une nouvelle approche probabiliste hybride pour l'organisation des collections d'images. Sur une collection d'images organisée hiérarchiquement en noeuds selon la sémantique des images, nous utilisons une approche générative pour l'estimation des mélanges de probabilités qui représentent l'apparence visuelle de chaque noeud dans la collection. Ensuite nous appliquons une approche discriminative pour l'estimation des poids des caractéristiques visuelles. L'idée dans notre travail, est de limiter la recherche seulement aux noeuds qui représentent mieux la sémantique de la requête, ce qui donne une propriété sémantique à la recherche et diminue le fossé sémantique causé par les caractéristiques de bas-niveau