A Comprehensive Survey on Deep Graph Representation Learning

Graph representation learning aims to effectively encode high-dimensional sparse graph-structured data into low-dimensional dense vectors, which is a fundamental task that has been widely studied in a range of fields, including machine learning and data mining. Classic graph embedding methods follow the basic idea that the embedding vectors of interconnected nodes in the graph can still maintain a relatively close distance, thereby preserving the structural information between the nodes in the graph. However, this is sub-optimal due to: (i) traditional methods have limited model capacity which limits the learning performance; (ii) existing techniques typically rely on unsupervised learning strategies and fail to couple with the latest learning paradigms; (iii) representation learning and downstream tasks are dependent on each other which should be jointly enhanced. With the remarkable success of deep learning, deep graph representation learning has shown great potential and advantages over shallow (traditional) methods, there exist a large number of deep graph representation learning techniques have been proposed in the past decade, especially graph neural networks. In this survey, we conduct a comprehensive survey on current deep graph representation learning algorithms by proposing a new taxonomy of existing state-of-the-art literature. Specifically, we systematically summarize the essential components of graph representation learning and categorize existing approaches by the ways of graph neural network architectures and the most recent advanced learning paradigms. Moreover, this survey also provides the practical and promising applications of deep graph representation learning. Last but not least, we state new perspectives and suggest challenging directions which deserve further investigations in the future

Contextual Person Identification in Multimedia Data

We propose methods to improve automatic person identification, regardless of the visibility of a face, by integration of multiple cues including multiple modalities and contextual information. We propose a joint learning approach using contextual information from videos to improve learned face models. Further, we integrate additional modalities in a global fusion framework. We evaluate our approaches on a novel TV series data set, consisting of over 100 000 annotated faces

Advances in Artificial Intelligence: Models, Optimization, and Machine Learning

The present book contains all the articles accepted and published in the Special Issue “Advances in Artificial Intelligence: Models, Optimization, and Machine Learning” of the MDPI Mathematics journal, which covers a wide range of topics connected to the theory and applications of artificial intelligence and its subfields. These topics include, among others, deep learning and classic machine learning algorithms, neural modelling, architectures and learning algorithms, biologically inspired optimization algorithms, algorithms for autonomous driving, probabilistic models and Bayesian reasoning, intelligent agents and multiagent systems. We hope that the scientific results presented in this book will serve as valuable sources of documentation and inspiration for anyone willing to pursue research in artificial intelligence, machine learning and their widespread applications

Semantic multimedia modelling & interpretation for search & retrieval

With the axiomatic revolutionary in the multimedia equip devices, culminated in the proverbial proliferation of the image and video data. Owing to this omnipresence and progression, these data become the part of our daily life. This devastating data production rate accompanies with a predicament of surpassing our potentials for acquiring this data. Perhaps one of the utmost prevailing problems of this digital era is an information plethora. Until now, progressions in image and video retrieval research reached restrained success owed to its interpretation of an image and video in terms of primitive features. Humans generally access multimedia assets in terms of semantic concepts. The retrieval of digital images and videos is impeded by the semantic gap. The semantic gap is the discrepancy between a user’s high-level interpretation of an image and the information that can be extracted from an image’s physical properties. Content- based image and video retrieval systems are explicitly assailable to the semantic gap due to their dependence on low-level visual features for describing image and content. The semantic gap can be narrowed by including high-level features. High-level descriptions of images and videos are more proficient of apprehending the semantic meaning of image and video content. It is generally understood that the problem of image and video retrieval is still far from being solved. This thesis proposes an approach for intelligent multimedia semantic extraction for search and retrieval. This thesis intends to bridge the gap between the visual features and semantics. This thesis proposes a Semantic query Interpreter for the images and the videos. The proposed Semantic Query Interpreter will select the pertinent terms from the user query and analyse it lexically and semantically. The proposed SQI reduces the semantic as well as the vocabulary gap between the users and the machine. This thesis also explored a novel ranking strategy for image search and retrieval. SemRank is the novel system that will incorporate the Semantic Intensity (SI) in exploring the semantic relevancy between the user query and the available data. The novel Semantic Intensity captures the concept dominancy factor of an image. As we are aware of the fact that the image is the combination of various concepts and among the list of concepts some of them are more dominant then the other. The SemRank will rank the retrieved images on the basis of Semantic Intensity. The investigations are made on the LabelMe image and LabelMe video dataset. Experiments show that the proposed approach is successful in bridging the semantic gap. The experiments reveal that our proposed system outperforms the traditional image retrieval systems

Shape Representations Using Nested Descriptors

The problem of shape representation is a core problem in computer vision. It can be argued that shape representation is the most central representational problem for computer vision, since unlike texture or color, shape alone can be used for perceptual tasks such as image matching, object detection and object categorization. This dissertation introduces a new shape representation called the nested descriptor. A nested descriptor represents shape both globally and locally by pooling salient scaled and oriented complex gradients in a large nested support set. We show that this nesting property introduces a nested correlation structure that enables a new local distance function called the nesting distance, which provides a provably robust similarity function for image matching. Furthermore, the nesting property suggests an elegant flower like normalization strategy called a log-spiral difference. We show that this normalization enables a compact binary representation and is equivalent to a form a bottom up saliency. This suggests that the nested descriptor representational power is due to representing salient edges, which makes a fundamental connection between the saliency and local feature descriptor literature. In this dissertation, we introduce three examples of shape representation using nested descriptors: nested shape descriptors for imagery, nested motion descriptors for video and nested pooling for activities. We show evaluation results for these representations that demonstrate state-of-the-art performance for image matching, wide baseline stereo and activity recognition tasks

Segmentation multi-vues d'objet

There has been a growing interest for multi-camera systems and many interesting works have tried to tackle computer vision problems in this particular configuration. The general objective is to propose new multi-view oriented methods instead of applying limited monocular approaches independently for each viewpoint. The work in this thesis is an attempt to have a better understanding of the multi-view object segmentation problem and to propose an alternative approach making maximum use of the available information from different viewpoints. Multiple view segmentation consists in segmenting objects simultaneously in several views. Classic monocular segmentation approaches reason on a single image and do not benefit from the presence of several viewpoints. A key issue in that respect is to ensure propagation of segmentation information between views while minimizing complexity and computational cost. In this work, we first investigate the idea that examining measurements at the projections of a sparse set of 3D points is sufficient to achieve this goal. The proposed algorithm softly assigns each of these 3D samples to the scene background if it projects on the background region in at least one view, or to the foreground if it projects on foreground region in all views. A complete probabilistic framework is proposed to estimate foreground/background color models and the method is tested on various datasets from state of the art. Two different extensions of the sparse 3D sampling segmentation framework are proposed in two scenarios. In the first, we show the flexibility of the sparse sampling framework, by using variational inference to integrate Gaussian mixture models as appearance models. In the second scenario, we propose a study of how to incorporate depth measurements in multi-view segmentation. We present a quantitative evaluation, showing that typical color-based segmentation robustness issues due to color-space ambiguity between foreground and background, can be at least partially mitigated by using depth, and that multi-view color depth segmentation also improves over monocular color depth segmentation strategies. The various tests also showed the limitations of the proposed 3D sparse sampling approach which was the motivation to propose a new method based on a richer description of image regions using superpixels. This model, that expresses more subtle relationships of the problem trough a graph construction linking superpixels and 3D samples, is one of the contributions of this work. In this new framework, time related information is also integrated. With static views, results compete with state of the art methods but they are achieved with significantly fewer viewpoints. Results on videos demonstrate the benefit of segmentation propagation through geometric and temporal cues. Finally, the last part of the thesis explores the possibilities of tracking in uncalibrated multi-view scenarios. A summary of existing methods in this field is presented, in both mono-camera and multi-camera scenarios. We investigate the potential of using self-similarity matrices to describe and compare motion in the context of multi-view tracking.L'utilisation de systèmes multi-caméras est de plus en plus populaire et il y a un intérêt croissant à résoudre les problèmes de vision par ordinateur dans ce contexte particulier. L'objectif étant de ne pas se limiter à l'application des méthodes monoculaires mais de proposer de nouvelles approches intrinsèquement orientées vers les systèmes multi-caméras. Le travail de cette thèse a pour objectif une meilleure compréhension du problème de segmentation multi-vues, pour proposer une nouvelle approche qui tire meilleur parti de la redondance d'information inhérente à l'utilisation de plusieurs points de vue. La segmentation multi-vues est l'identification de l'objet observé simultanément dans plusieurs caméras et sa séparation de l'arrière-plan. Les approches monoculaires classiques raisonnent sur chaque image de manière indépendante et ne bénéficient pas de la présence de plusieurs points de vue. Une question clé de la segmentation multi-vues réside dans la propagation d'information sur la segmentation entres les images tout en minimisant la complexité et le coût en calcul. Dans ce travail, nous investiguons en premier lieu l'utilisation d'un ensemble épars d'échantillons de points 3D. L'algorithme proposé classe chaque point comme "vide" s'il se projette sur une région du fond et "occupé" s'il se projette sur une région avant-plan dans toutes les vues. Un modèle probabiliste est proposé pour estimer les modèles de couleur de l'avant-plan et de l'arrière-plan, que nous testons sur plusieurs jeux de données de l'état de l'art. Deux extensions du modèle sont proposées. Dans la première, nous montrons la flexibilité de la méthode proposée en intégrant les mélanges de Gaussiennes comme modèles d'apparence. Cette intégration est possible grâce à l'utilisation de l'inférence variationelle. Dans la seconde, nous montrons que le modèle bayésien basé sur les échantillons 3D peut aussi être utilisé si des mesures de profondeur sont présentes. Les résultats de l'évaluation montrent que les problèmes de robustesse, typiquement causés par les ambigüités couleurs entre fond et forme, peuvent être au moins partiellement résolus en utilisant cette information de profondeur. A noter aussi qu'une approche multi-vues reste meilleure qu'une méthode monoculaire utilisant l'information de profondeur. Les différents tests montrent aussi les limitations de la méthode basée sur un échantillonnage éparse. Cela a montré la nécessité de proposer un modèle reposant sur une description plus riche de l'apparence dans les images, en particulier en utilisant les superpixels. L'une des contributions de ce travail est une meilleure modélisation des contraintes grâce à un schéma par coupure de graphes liant les régions d'images aux échantillons 3D. Dans le cas statique, les résultats obtenus rivalisent avec ceux de l'état de l'art mais sont obtenus avec beaucoup moins de points de vue. Les résultats dans le cas dynamique montrent l'intérêt de la propagation de l'information de segmentation à travers la géométrie et le mouvement. Enfin, la dernière partie de cette thèse explore la possibilité d'améliorer le suivi dans les systèmes multi-caméras non calibrés. Un état de l'art sur le suivi monoculaire et multi-caméras est présenté et nous explorons l'utilisation des matrices d'autosimilarité comme moyen de décrire le mouvement et de le comparer entre plusieurs caméras

AVATAR - Machine Learning Pipeline Evaluation Using Surrogate Model

© 2020, The Author(s). The evaluation of machine learning (ML) pipelines is essential during automatic ML pipeline composition and optimisation. The previous methods such as Bayesian-based and genetic-based optimisation, which are implemented in Auto-Weka, Auto-sklearn and TPOT, evaluate pipelines by executing them. Therefore, the pipeline composition and optimisation of these methods requires a tremendous amount of time that prevents them from exploring complex pipelines to find better predictive models. To further explore this research challenge, we have conducted experiments showing that many of the generated pipelines are invalid, and it is unnecessary to execute them to find out whether they are good pipelines. To address this issue, we propose a novel method to evaluate the validity of ML pipelines using a surrogate model (AVATAR). The AVATAR enables to accelerate automatic ML pipeline composition and optimisation by quickly ignoring invalid pipelines. Our experiments show that the AVATAR is more efficient in evaluating complex pipelines in comparison with the traditional evaluation approaches requiring their execution

Segmentation multi-vues d'objet

There has been a growing interest for multi-camera systems and many interesting works have tried to tackle computer vision problems in this particular configuration. The general objective is to propose new multi-view oriented methods instead of applying limited monocular approaches independently for each viewpoint. The work in this thesis is an attempt to have a better understanding of the multi-view object segmentation problem and to propose an alternative approach making maximum use of the available information from different viewpoints.Multiple view segmentation consists in segmenting objects simultaneously in several views. Classic monocular segmentation approaches reason on a single image and do not benefit from the presence of several viewpoints. A key issue in that respect is to ensure propagation of segmentation information between views while minimizing complexity and computational cost. In this work, we first investigate the idea that examining measurements at the projections of a sparse set of 3D points is sufficient to achieve this goal. The proposed algorithm softly assigns each of these 3D samples to the scene background if it projects on the background region in at least one view, or to the foreground if it projects on foreground region in all views. A complete probabilistic framework is proposed to estimate foreground/background color models and the method is tested on various datasets from state of the art.Two different extensions of the sparse 3D sampling segmentation framework are proposed in two scenarios. In the first, we show the flexibility of the sparse sampling framework, by using variational inference to integrate Gaussian mixture models as appearance models. In the second scenario, we propose a study of how to incorporate depth measurements in multi-view segmentation. We present a quantitative evaluation, showing that typical color-based segmentation robustness issues due to color-space ambiguity between foreground and background, can be at least partially mitigated by using depth, and that multi-view color depth segmentation also improves over monocular color depth segmentation strategies.The various tests also showed the limitations of the proposed 3D sparse sampling approach which was the motivation to propose a new method based on a richer description of image regions using superpixels. This model, that expresses more subtle relationships of the problem trough a graph construction linking superpixels and 3D samples, is one of the contributions of this work. In this new framework, time related information is also integrated. With static views, results compete with state of the art methods but they are achieved with significantly fewer viewpoints. Results on videos demonstrate the benefit of segmentation propagation through geometric and temporal cues.Finally, the last part of the thesis explores the possibilities of tracking in uncalibrated multi-view scenarios. A summary of existing methods in this field is presented, in both mono-camera and multi-camera scenarios. We investigate the potential of using self-similarity matrices to describe and compare motion in the context of multi-view tracking.L’utilisation de systèmes multi-caméras est de plus en plus populaire et il y a un intérêt croissant à résoudre les problèmes de vision par ordinateur dans ce contexte particulier. L’objectif étant de ne pas se limiter à l’application des méthodes monoculaires mais de proposer de nouvelles approches intrinsèquement orientées vers les systèmes multi-caméras. Le travail de cette thèse a pour objectif une meilleure compréhension du problème de segmentation multi-vues, pour proposer une nouvelle approche qui tire meilleur parti de la redondance d’information inhérente à l’utilisation de plusieurs points de vue.La segmentation multi-vues est l’identification de l’objet observé simultanément dans plusieurs caméras et sa séparation de l’arrière-plan. Les approches monoculaires classiques raisonnent sur chaque image de manière indépendante et ne bénéficient pas de la présence de plusieurs points de vue. Une question clé de la segmentation multi-vues réside dans la propagation d’information sur la segmentation entres les images tout en minimisant la complexité et le coût en calcul. Dans ce travail, nous investiguons en premier lieu l’utilisation d’un ensemble épars d’échantillons de points 3D. L’algorithme proposé classe chaque point comme "vide" s’il se projette sur une région du fond et "occupé" s’il se projette sur une région avant-plan dans toutes les vues. Un modèle probabiliste est proposé pour estimer les modèles de couleur de l’avant-plan et de l’arrière-plan, que nous testons sur plusieurs jeux de données de l’état de l’art.Deux extensions du modèle sont proposées. Dans la première, nous montrons la flexibilité de la méthode proposée en intégrant les mélanges de Gaussiennes comme modèles d’apparence. Cette intégration est possible grâce à l’utilisation de l’inférence variationelle. Dans la seconde, nous montrons que le modèle bayésien basé sur les échantillons 3D peut aussi être utilisé si des mesures de profondeur sont présentes. Les résultats de l’évaluation montrent que les problèmes de robustesse, typiquement causés par les ambigüités couleurs entre fond et forme, peuvent être au moins partiellement résolus en utilisant cette information de profondeur. A noter aussi qu’une approche multi-vues reste meilleure qu’une méthode monoculaire utilisant l’information de profondeur.Les différents tests montrent aussi les limitations de la méthode basée sur un échantillonnage éparse. Cela a montré la nécessité de proposer un modèle reposant sur une description plus riche de l’apparence dans les images, en particulier en utilisant les superpixels. L’une des contributions de ce travail est une meilleure modélisation des contraintes grâce à un schéma par coupure de graphes liant les régions d’images aux échantillons 3D. Dans le cas statique, les résultats obtenus rivalisent avec ceux de l’état de l’art mais sont obtenus avec beaucoup moins de points de vue. Les résultats dans le cas dynamique montrent l’intérêt de la propagation de l’information de segmentation à travers la géométrie et le mouvement.Enfin, la dernière partie de cette thèse explore la possibilité d’améliorer le suivi dans les systèmes multi-caméras non calibrés. Un état de l’art sur le suivi monoculaire et multi-caméras est présenté et nous explorons l’utilisation des matrices d’autosimilarité comme moyen de décrire le mouvement et de le comparer entre plusieurs caméras

Fuzzy Techniques for Decision Making 2018

Zadeh's fuzzy set theory incorporates the impreciseness of data and evaluations, by imputting the degrees by which each object belongs to a set. Its success fostered theories that codify the subjectivity, uncertainty, imprecision, or roughness of the evaluations. Their rationale is to produce new flexible methodologies in order to model a variety of concrete decision problems more realistically. This Special Issue garners contributions addressing novel tools, techniques and methodologies for decision making (inclusive of both individual and group, single- or multi-criteria decision making) in the context of these theories. It contains 38 research articles that contribute to a variety of setups that combine fuzziness, hesitancy, roughness, covering sets, and linguistic approaches. Their ranges vary from fundamental or technical to applied approaches