Image annotation and retrieval based on multi-modal feature clustering and similarity propagation.

The performance of content-based image retrieval systems has proved to be inherently constrained by the used low level features, and cannot give satisfactory results when the user\u27s high level concepts cannot be expressed by low level features. In an attempt to bridge this semantic gap, recent approaches started integrating both low level-visual features and high-level textual keywords. Unfortunately, manual image annotation is a tedious process and may not be possible for large image databases. In this thesis we propose a system for image retrieval that has three mains components. The first component of our system consists of a novel possibilistic clustering and feature weighting algorithm based on robust modeling of the Generalized Dirichlet (GD) finite mixture. Robust estimation of the mixture model parameters is achieved by incorporating two complementary types of membership degrees. The first one is a posterior probability that indicates the degree to which a point fits the estimated distribution. The second membership represents the degree of typicality and is used to indentify and discard noise points. Robustness to noisy and irrelevant features is achieved by transforming the data to make the features independent and follow Beta distribution, and learning optimal relevance weight for each feature subset within each cluster. We extend our algorithm to find the optimal number of clusters in an unsupervised and efficient way by exploiting some properties of the possibilistic membership function. We also outline a semi-supervised version of the proposed algorithm. In the second component of our system consists of a novel approach to unsupervised image annotation. Our approach is based on: (i) the proposed semi-supervised possibilistic clustering; (ii) a greedy selection and joining algorithm (GSJ); (iii) Bayes rule; and (iv) a probabilistic model that is based on possibilistic memebership degrees to annotate an image. The third component of the proposed system consists of an image retrieval framework based on multi-modal similarity propagation. The proposed framework is designed to deal with two data modalities: low-level visual features and high-level textual keywords generated by our proposed image annotation algorithm. The multi-modal similarity propagation system exploits the mutual reinforcement of relational data and results in a nonlinear combination of the different modalities. Specifically, it is used to learn the semantic similarities between images by leveraging the relationships between features from the different modalities. The proposed image annotation and retrieval approaches are implemented and tested with a standard benchmark dataset. We show the effectiveness of our clustering algorithm to handle high dimensional and noisy data. We compare our proposed image annotation approach to three state-of-the-art methods and demonstrate the effectiveness of the proposed image retrieval system

A stochastic-variational model for soft Mumford-Shah segmentation

In contemporary image and vision analysis, stochastic approaches demonstrate great flexibility in representing and modeling complex phenomena, while variational-PDE methods gain enormous computational advantages over Monte-Carlo or other stochastic algorithms. In combination, the two can lead to much more powerful novel models and efficient algorithms. In the current work, we propose a stochastic-variational model for soft (or fuzzy) Mumford-Shah segmentation of mixture image patterns. Unlike the classical hard Mumford-Shah segmentation, the new model allows each pixel to belong to each image pattern with some probability. We show that soft segmentation leads to hard segmentation, and hence is more general. The modeling procedure, mathematical analysis, and computational implementation of the new model are explored in detail, and numerical examples of synthetic and natural images are presented.Comment: 22 page

Unsupervised Selection and Estimation of Non-Gaussian Mixtures for High Dimensional Data Analysis

Lately, the enormous generation of databases in almost every aspect of life has created a great demand for new, powerful tools for turning data into useful information. Therefore, researchers were encouraged to explore and develop new machine learning ideas and methods. Mixture models are one of the machine learning techniques receiving considerable attention due to their ability to handle efficiently and effectively multidimensional data. Generally, four critical issues have to be addressed when adopting mixture models in high dimensional spaces: (1) choice of the probability density functions, (2) estimation of the mixture parameters, (3) automatic determination of the number of components M in the mixture, and (4) determination of what features best discriminate among the different components. The main goal of this thesis is to summarize all these challenging interrelated problems in one unified model. In most of the applications, the Gaussian density is used in mixture modeling of data. Although a Gaussian mixture may provide a reasonable approximation to many real-world distributions, it is certainly not always the best approximation especially in computer vision and image processing applications where we often deal with non-Gaussian data. Therefore, we propose to use three highly flexible distributions: the generalized Gaussian distribution (GGD), the asymmetric Gaussian distribution (AGD), and the asymmetric generalized Gaussian distribution (AGGD). We are motivated by the fact that these distributions are able to fit many distributional shapes and then can be considered as a useful class of flexible models to address several problems and applications involving measurements and features having well-known marked deviation from the Gaussian shape. Recently, researches have shown that model selection and parameter learning are highly dependent and should be performed simultaneously. For this purpose, many approaches have been suggested. The vast majority of these approaches can be classified, from a computational point of view, into two classes: deterministic and stochastic methods. Deterministic methods estimate the model parameters for a set of candidate models using the Expectation-Maximization (EM) framework, then choose the model that maximizes a model selection criterion. Stochastic methods such as Markov chain Monte Carlo (MCMC) can be used in order to sample from the full a posteriori distribution with M considered unknown. Hence, in this thesis, we propose three learning techniques capable of automatically determining model complexity while learning its parameters. First, we incorporate a Minimum Message Length (MML) penalty in the model learning step performed using the EM algorithm. Our second approach employs the Rival Penalized EM (RPEM) algorithm which is able to select an appropriate number of densities by fading out the redundant densities from a density mixture. Last but not least, we incorporate the nonparametric aspect of mixture models by assuming a countably infinite number of components and using Markov Chain Monte Carlo (MCMC) simulations for the estimation of the posterior distributions. Hence, the difficulty of choosing the appropriate number of clusters is sidestepped by assuming that there are an infinite number of mixture components. Another essential issue in the case of statistical modeling in general and finite mixtures in particular is feature selection (i.e. identification of the relevant or discriminative features describing the data) especially in the case of high-dimensional data. Indeed, feature selection has been shown to be a crucial step in several image processing, computer vision and pattern recognition applications not only because it speeds up learning but also because it improves model accuracy and generalization. Moreover, the learning of the mixture parameters ( i.e. both model selection and parameters estimation) is greatly affected by the quality of the features used. Hence, in this thesis, we are trying to solve the feature selection problem in unsupervised learning by casting it as an estimation problem, thus avoiding any combinatorial search. Finally, the effectiveness of our approaches is evaluated by applying them to different computer vision and image processing applications

Segmentation d'images et suivi d'objets en vidéos approches par estimation, sélection de caractéristiques et contours actifs

Cette thèse aborde deux problèmes parmi les plus importants et les plus complexes dans la vision artificielle, qui sont la segmentation d'images et le suivi d'objets dans les vidéos. Nous proposons plusieurs approches, traitant de ces deux problèmes, qui sont basées sur la modélisation variationnelle (contours actifs) et statistique. Ces approches ont pour but de surmonter différentes limites théoriques et pratiques (algorithmiques) de ces deux problèmes. En premier lieu, nous abordons le problème d'automatisation de la segmentation par contours actifs"ensembles de niveaux", et sa généralisation pour le cas de plusieurs régions. Pour cela, un modèle permettant d'estimer l'information de régions de manière automatique, et adaptative au contenu de l'image, est proposé. Ce modèle n'utilise aucune information a priori sur les régions, et traite également les images de couleur et de texture, avec un nombre arbitraire de régions. Nous introduisons ensuite une approche statistique pour estimer et intégrer la pertinence des caractéristiques et la sémantique dans la segmentation d'objets d'intérêt. En deuxième lieu, nous abordons le problème du suivi d'objets dans les vidéos en utilisant les contours actifs. Nous proposons pour cela deux modèles différents. Le premier suppose que les propriétés photométriques des objets suivis sont invariantes dans le temps, mais le modèle est capable de suivre des objets en présence de bruit, et au milieu de fonds de vidéos non-statiques et encombrés. Ceci est réalisé grâce à l'intégration de l'information de régions, de frontières et de formes des objets suivis. Le deuxième modèle permet de prendre en charge les variations photométriques des objets suivis, en utilisant un modèle statistique adaptatif à l'apparence de ces derniers. Finalement, nous proposons un nouveau modèle statistique, basé sur la Gaussienne généralisée, pour une représentation efficace de données bruitées et de grandes dimensions en segmentation. Ce modèle est utilisé pour assurer la robustesse de la segmentation des images de couleur contenant du bruit, ainsi que des objets en mouvement dans les vidéos (acquises par des caméras statiques) contenant de l'ombrage et/ou des changements soudains d'illumination

Rain Removal in Traffic Surveillance: Does it Matter?

Varying weather conditions, including rainfall and snowfall, are generally regarded as a challenge for computer vision algorithms. One proposed solution to the challenges induced by rain and snowfall is to artificially remove the rain from images or video using rain removal algorithms. It is the promise of these algorithms that the rain-removed image frames will improve the performance of subsequent segmentation and tracking algorithms. However, rain removal algorithms are typically evaluated on their ability to remove synthetic rain on a small subset of images. Currently, their behavior is unknown on real-world videos when integrated with a typical computer vision pipeline. In this paper, we review the existing rain removal algorithms and propose a new dataset that consists of 22 traffic surveillance sequences under a broad variety of weather conditions that all include either rain or snowfall. We propose a new evaluation protocol that evaluates the rain removal algorithms on their ability to improve the performance of subsequent segmentation, instance segmentation, and feature tracking algorithms under rain and snow. If successful, the de-rained frames of a rain removal algorithm should improve segmentation performance and increase the number of accurately tracked features. The results show that a recent single-frame-based rain removal algorithm increases the segmentation performance by 19.7% on our proposed dataset, but it eventually decreases the feature tracking performance and showed mixed results with recent instance segmentation methods. However, the best video-based rain removal algorithm improves the feature tracking accuracy by 7.72%.Comment: Published in IEEE Transactions on Intelligent Transportation System