Bandwidth selection for kernel estimation in mixed multi-dimensional spaces

Kernel estimation techniques, such as mean shift, suffer from one major drawback: the kernel bandwidth selection. The bandwidth can be fixed for all the data set or can vary at each points. Automatic bandwidth selection becomes a real challenge in case of multidimensional heterogeneous features. This paper presents a solution to this problem. It is an extension of \cite{Comaniciu03a} which was based on the fundamental property of normal distributions regarding the bias of the normalized density gradient. The selection is done iteratively for each type of features, by looking for the stability of local bandwidth estimates across a predefined range of bandwidths. A pseudo balloon mean shift filtering and partitioning are introduced. The validity of the method is demonstrated in the context of color image segmentation based on a 5-dimensional space

How digital will the future be? Analysis of prospective scenarios

With the climate change context, many prospective studies, generally encompassing all areas of society, imagine possible futures to expand the range of options. The role of digital technologies within these possible futures is rarely specifically targeted. Which digital technologies and methodologies do these studies envision in a world that has mitigated and adapted to climate change? In this paper, we propose a typology for scenarios to survey digital technologies and their applications in 14 prospective studies and their corresponding 35 future scenarios. Our finding is that all the scenarios consider digital technology to be present in the future. We observe that only a few of them question our relationship with digital technology and all aspects related to its materiality, and none of the general studies envision breakthroughs concerning technologies used today. Our result demonstrates the lack of a systemic view of information and communication technologies. We therefore argue for new prospective studies to envision the future of ICT

How to estimate carbon footprint when training deep learning models? A guide and review

Machine learning and deep learning models have become essential in the recent fast development of artificial intelligence in many sectors of the society. It is now widely acknowledge that the development of these models has an environmental cost that has been analyzed in many studies. Several online and software tools have been developed to track energy consumption while training machine learning models. In this paper, we propose a comprehensive introduction and comparison of these tools for AI practitioners wishing to start estimating the environmental impact of their work. We review the specific vocabulary, the technical requirements for each tool. We compare the energy consumption estimated by each tool on two deep neural networks for image processing and on different types of servers. From these experiments, we provide some advice for better choosing the right tool and infrastructure.Comment: Environmental Research Communications, 202

Estimation à noyau adaptatif dans des espaces multidimensionnels hétérogènes

Les méthodes d'estimation à noyau, telles que le mean shift, ont un inconvénient majeur : le choix de la taille du noyau. La sélection de cette taille devient vraiment difficile dans le cas de données multidimensionnelles et hétérogènes. Nous présentons une solution à ce problème. La taille est choisie itérativement pour chaque type de données, en cherchant parmi un ensemble de tailles prédéfinies celle qui donne localement les résultats les plus stables. La sélection itérative nécessite l'introduction d'un nouvel estimateur. La méthode est validée dans le contexte de la segmentation d'image couleur et de mouvement

Prozessoptimierung im Werttransportunternehmen

nicht vorhande

Sélection de la taille du noyau pour l'estimation à noyau dans des espaces multidimensionnels hétérogènes.

Kernel estimation techniques, such as mean shift, suffer from one major drawback: the kernel bandwidth selection. This selection becomes a real challenge in case of multidimensional heterogeneous features. This paper presents a solution to this problem. The selection is done iteratively for each type of features, by looking for the stability of local bandwidth estimates within a predefined range of bandwidths. A new estimator that permits the iterative computation is introduced. The validity of the method is demonstrated in the context of color image segmentation and motion segmentation.Les méthodes d'estimation à noyau, telles que le mean shift, ont un inconvénient majeur : le choix de la taille du noyau. La sélection de cette taille devient vraiment difficile dans le cas de données multidimensionnelles et hétérogènes. Nous présentons une solution à ce problème. La taille est choisie itérativement pour chaque type de données, en cherchant parmi un ensemble de tailles prédéfinies celle qui donne localement les résultats les plus stables. La sélection itérative nécessite l'introduction d'un nouvel estimateur. La méthode est validée dans le contexte de la segmentation d'image couleur et de mouvement

Detection and segmentation of moving objects in complex scenes

International audienceIn this paper, we address the difficult task of detecting and segmenting foreground moving objects in complex scenes. The sequences we consider exhibit highly dynamic backgrounds, illumination changes and low contrasts, and can have been shot by a moving camera. Three main steps compose the proposed method. First, a set of moving points is selected within a sub-grid of image pixels. A multi-cue descriptor is associated to each of these points. Clusters of points are then formed using a variable bandwidth mean shift technique with automatic bandwidth selection. Finally, segmentation of the object associated to a given cluster is performed using graph cuts. Experiments and comparisons to other motion detection methods on challenging sequences demonstrate the performance of the proposed method for video analysis in complex scenes

Facial Action Units Intensity Estimation by the Fusion of Features with Multi-kernel Support Vector Machine

International audience— Automatic facial expression recognition has emerged over two decades. The recognition of the posed facial expressions and the detection of Action Units (AUs) of facial expression have already made great progress. More recently, the automatic estimation of the variation of facial expression, either in terms of the intensities of AUs or in terms of the values of dimensional emotions, has emerged in the field of the facial expression analysis. However, discriminating different intensities of AUs is a far more challenging task than AUs detection due to several intractable problems. Aiming to continuing standardized evaluation procedures and surpass the limits of the current research, the second Facial Expression Recognition and Analysis challenge (FERA2015) is presented. In this context, we propose a method using the fusion of the different appearance and geometry features based on a multi-kernel Support Vector Machine (SVM) for the automatic estimation of the intensities of the AUs. The result of our approach benefiting from taking advantages of the different features adapting to a multi-kernel SVM is shown to outperform the conventional methods based on the mono-type feature with single kernel SVM