Representing an image as a graph captures its spatial and contextual relationships effectively.Using Graph Neural Networks (GNNs) on graph-based images has considerably enhanced image segmentation.This paper investigates Multi-View GNNs for image segmentation, comparing Intermediate and Late Fusion methods.Experiments show that Intermediate Fusion achieves high accuracy on synthetic data by integrating relational features upfront.On a real dataset, Late Fusion methods, particularly RVCons, outperform Intermediate Fusion by dynamically aggregating multi-view predictions.Indeed, Late Fusion effectively mitigates issues arising from view-specific noise and variance.The results underscore the complementary strengths of both fusion strategies.

Recently, many studies have been conducted on automated epileptic seizures detection. However, few of these techniques are applied in clinical settings for several reasons. One of them is the imbalanced nature of the seizure detection task. Additionally, the current detection techniques do not really generalize to other patient populations. To address these issues, we present in this paper a hybrid CNN-LSTM model robust to cross-site variability. We investigate the use of data augmentation (DA) methods as an efficient tool to solve imbalanced training problems. The model trained on the publicly Children’s Hospital of Boston (CHB) data set achieved great performance on a french data set acquired at the Centre Hospitalier Universitaire of Angers (CHU). Results showed that this approach outperforms both other deep learning (DL) and state-of-the-art methods.

Recently, one-step clustering methods have shown good performance. However, very few one-step approaches have addressed the multi-view case, where an instance may have multiple representations. Data can be represented with multiple heterogeneous views. Clustering with multiple views faces the challenge of how to combine all the different views. A general scheme is to represent the views by view-based graphs and/or a consensus graph. Graphs can be well suited for clustering problems since they can capture the local and global structure of the data. In this paper, we present a novel approach to one-step graph-based multi-view clustering. In contrast to existing graph-based one-step clustering methods, our proposed method introduces two key innovations. First, we build an additional graph by using the cluster label correlation to the graphs associated with the data space. Second, a smoothing constraint is exploited to constrain the cluster-label matrix and make it more consistent with the original data graphs as well as with and label graphs. Experimental results on several public datasets show the efficiency of the proposed approach. All cluster evaluation metrics show significant improvement by applying our method to different types and sizes of datasets. The average improvement (across all datasets) is the difference between the indicator obtained by our approach and the indicator obtained by the most competitive method. The average improvement is approximately 4%, 2%, 3%, and 2% for the Accuracy indicator, the Normalized Mutual Information indicator, the Purity indicator, and the Adjusted Rand index, respectively.

Recently, multi-view clustering has received much attention in the fields of machine learning and pattern recognition. Spectral clustering for single and multiple views has been the common solution. Despite its good clustering performance, it has a major limitation: it requires an extra step of clustering. This extra step, which could be the famous k-means clustering, depends heavily on initialization, which may affect the quality of the clustering result. To overcome this problem, a new method called Multi-view Clustering via Consensus Graph Learning and Nonnegative Embedding (MVCGE) is presented in this paper. In the proposed approach, the consensus affinity matrix (graph matrix), consensus representation and cluster index matrix (nonnegative embedding) are learned simultaneously in a unified framework. Our proposed method takes as input the different kernel matrices corresponding to the different views. The proposed learning model integrates two interesting constraints: (i) the cluster indices should be as smooth as possible over the consensus graph and (ii) the cluster indices are set to be as close as possible to the graph convolution of the consensus representation. In this approach, no post-processing such as k-means or spectral rotation is required. Our approach is tested with real and synthetic datasets. The experiments performed show that the proposed method performs well compared to many state-of-the-art approaches.

Today, deep convolutional neural networks (CNNs) have demonstrated state of the art performance for supervised medical image segmentation, across various imaging modalities and tasks. Despite early success, segmentation networks may still generate anatomically aberrant segmentations, with holes or inaccuracies near the object boundaries. To mitigate this effect, recent research works have focused on incorporating spatial information or prior knowledge to enforce anatomically plausible segmentation. If the integration of prior knowledge in image segmentation is not a new topic in classical optimization approaches, it is today an increasing trend in CNN based image segmentation, as shown by the growing literature on the topic. In this survey, we focus on high level prior, embedded at the loss function level. We categorize the articles according to the nature of the prior: the object shape, size, topology, and the inter-regions constraints. We highlight strengths and limitations of current approaches, discuss the challenge related to the design and the integration of prior-based losses, and the optimization strategies, and draw future research directions.

Person re-identification is one of the indispensable elements for visual surveillance. It assigns consistent labeling for the same person within the field of view of the same camera or even across multiple cameras. While handcrafted feature extraction is certainly one way of approaching this problem, in many cases, these features are becoming more and more complex. Besides, training a deep convolutional neural network (CNN) from scratch is difficult because it requires a large amount of labeled training data and a great deal of expertise to ensure proper convergence. This paper explores the following three main strategies for solving the person re-identification problem: (i) using handcrafted features, (ii) using transfer learning based on a pre-trained deep CNN (trained for object categorization) and (iii) training a deep CNN from scratch. Our experiments consistently demonstrated that: (1) The handcrafted features may still have favorable characteristics and benefits especially in cases where the learning database is not sufficient to train a deep network. (2) A fully trained Siamese CNN outperforms handcrafted approaches and the combination of pre-trained CNN with different re-identification processes. (3) Moreover, our experiments demonstrated that pre-trained features and handcrafted features perform equally well. These experiments have also revealed the most discriminative parts in the human body.

During the last two decades, a variety of models have been applied to understand and predict changes in land use. These models assign a single-attribute label to each spatial unit at any particular time of the simulation. This is not realistic because mixed use of land is quite common. A more detailed classification allowing the modelling of mixed land use would be desirable for better understanding and interpreting the evolution of the use of land. A possible solution is the multi-label (ML) concept where each spatial unit can belong to multiple classes simultaneously. For example, a cluster of summer houses at a lake in a forested area should be classified as water, forest and residential (built-up). The ML concept was introduced recently, and it belongs to the machine learning field. In this article, the ML concept is introduced and applied in land-use modelling. As a novelty, we present a land-use change model that allows ML class assignment using the k nearest neighbour (kNN) method that derives a functional relationship between land use and a set of explanatory variables. A case study with a rich data-set from Luxembourg using biophysical data from aerial photography is described. The model achieves promising results based on the well-known ML evaluation criteria. The application described in this article highlights the value of the multi-label k nearest neighbour method (MLkNN) for land-use modelling.

This article presents an application of a new approach combining the bayesian framework with interval methods over vehicle state estimation. Interval state estimation seems more guaranted than a point state estimation when the system dynamics and measurement models have interval types of uncertainties. Firstly, a brief description about the Box Particle Filter (BPF) based on interval analysis is introduced. Secondly, the model of the vehicle and the state observer are presented. The performance of the BPF is studied and compared with that of the Kalman filter. Finally, some results of the vehicle dynamic estimation with simulated data are presented and interpreted.

The goal of map-matching algorithms is to identify the road taken by a vehicle and to compute an estimate of the vehicle position on that road using a digital map. In this paper, a map-matching algorithm based on interval analysis and the belief function theory is proposed. The method combines the outputs from existing bounded-error estimation techniques with piecewise rectangular roads that are selected using evidential reasoning. A set of candidate roads is first defined at each time step using the topology of the map and a similarity criterion, and a mass function on the set of candidate roads is computed. An overall estimate of the vehicle position is then derived after the most probable candidate road has been selected. This method allows multiple road junction hypotheses to efficiently be handled and can cope with missing data. In addition, the implementation of the method is quite simple, because it is based on geometrical properties of boxes and rectangular road segments. Experiments with simulated and real data demonstrate the ability of this method to handle junction situations and to compute an accurate estimate of the vehicle position.

Multi-label learning is increasingly required by many applications where instances may belong to several classes at the same time. In this paper, we propose a fuzzy k-nearest neighbor method for multi-label classification using the veristic variable framework. Veristic variables are variables that can assume simultaneously multiple values with different degrees. In multi-label learning, class labels can be considered as veristic variables since each instance can belong simultaneously to more than one class. Several applications on benchmark datasets demonstrate the efficiency of our approach.

A new approach to nonlinear state estimation based on belief-function theory and interval analysis is presented. This method uses belief structures composed of a finite number of axis-aligned boxes with associated masses. Such belief structures can represent partial information on model and measurement uncertainties more accurately than can the bounded-error approach alone. Focal sets are propagated in system equations using interval arithmetics and constraint-satisfaction techniques, thus generalizing pure interval analysis. This model was used to locate a land vehicle using a dynamic fusion of Global Positioning System measurements with dead reckoning sensors. The method has been shown to provide more accurate estimates of vehicle position than does the bounded-error method while retaining what is essential: providing guaranteed computations. The performances of our method were also slightly better than those of a particle filter, with comparable running time. These results suggest that our method is a viable alternative to both bounded-error and probabilistic Monte Carlo approaches for vehicle-localization applications.

In this contribution, we propose an original algorithm for self-localization in mobile ad-hoc networks. The proposed technique, based on interval analysis, is suited to the limited computational and memory resources of mobile nodes. The incertitude about the estimated position of each node is propagated in an interval form. The propagation is based on a state space model and formulated by a constraints satisfaction problem. Observations errors as well as anchor nodes imperfections are taken into account in a simple and computational-consistent way. A simple Waltz algorithm is then applied in order to contract the solution, yielding a guaranteed and robust online estimation of the mobile node position. Simulation results on mobile node group trajectories corroborate the efficiency of the proposed technique and show that it compares favorably to particle filtering methods.

Over the last years, particle filters (PF) have attracted considerable attention in the field of nonlinear state estimation due to their relaxation of the linear and Gaussian restrictions in the state space model. However, for some applications, PF are not adapted for a real-time implementation. In this paper we propose a new method, called box particle filter (BPF), for dynamic nonlinear state estimation, which is based on particle filters and interval frameworks and which is well adapted for real time applications. Interval framework will allow to explain regions with high likelihood by a small number of box particles instead of a large number of particles in the case of PF. Experiments on real data for global localization of a vehicle show the usefulness and the efficiency of the proposed approach.

This paper presents a new fusion strategy that mixes interval analysis techniques and particle filters for data fusion and state estimation purposes occurring in many robotic perception problems. The method requires a small number of "box particles". This, in fact, answers one of major problems when using particle filters techniques. We report the case study of a land vehicle localization problem and we make a comparison based on real data between the performance of a particle filter and the new developed strategy.

Linear discriminant analysis (LDA) is a standard statistical tool for data analysis. Recently, a method called generalized discriminant analysis (GDA) has been developed to deal with nonlinear discriminant analysis using kernel functions. Difficulties for the GDA method can arise in the form of both computational complexity and storage requirements. We present a sequential algorithm for GDA avoiding these problems when one deals with large numbers of datapoints.

Support vector machines (SVMs) are the most well known nonlinear classifiers based on the Mercer kernel trick. They generally lead to very sparse solutions that ensure good generalization performance. Recently, S. Mika et al. (see Advances in Neural Networks for Signal Processing, p.41-8, 1999) proposed a new nonlinear technique based on the kernel trick and the Fisher criterion: the nonlinear kernel Fisher discriminant (KFD). Experiments show that KFD is competitive with SVM classifiers. Nevertheless, it can be shown that there exist distributions such that even though the two classes are linearly separable, the Fisher linear discriminant has an error probability close to 1. We propose an alternative strategy based on Mercer kernels that consists in picking the optimum nonlinear receiver in the sense of the best second-order criterion. We also present a strategy for controlling the complexity of the resulting classifier. Finally, we compare this new method with SVM and KFD.

Statistical detection theories lead to the fundamental result that the optimum test consists in comparing any strictly monotone function of the likelihood ratio with a threshold value. In many applications, implementing such a test may be impossible. Therefore, we are often led to consider a simpler procedure for designing detectors. In particular, we can use alternative design criteria such as second-order measures of quality. A necessary and sufficient condition is given for such criteria to guarantee the best solution in the sense of classical detection theories. This result is illustrated by discussing the relevance of well-known criteria.