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 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

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

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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

Multi-Layer Local Graph Words for Object Recognition

In this paper, we propose a new multi-layer structural approach for the task of object based image retrieval. In our work we tackle the problem of structural organization of local features. The structural features we propose are nested multi-layered local graphs built upon sets of SURF feature points with Delaunay triangulation. A Bag-of-Visual-Words (BoVW) framework is applied on these graphs, giving birth to a Bag-of-Graph-Words representation. The multi-layer nature of the descriptors consists in scaling from trivial Delaunay graphs - isolated feature points - by increasing the number of nodes layer by layer up to graphs with maximal number of nodes. For each layer of graphs its own visual dictionary is built. The experiments conducted on the SIVAL and Caltech-101 data sets reveal that the graph features at different layers exhibit complementary performances on the same content and perform better than baseline BoVW approach. The combination of all existing layers, yields significant improvement of the object recognition performance compared to single level approaches.Comment: International Conference on MultiMedia Modeling, Klagenfurt : Autriche (2012

Fast Image and LiDAR alignment based on 3D rendering in sensor topology

Mobile Mapping Systems are now commonly used in large urban acquisition campaigns. They are often equiped with LiDAR sensors and optical cameras, providing very large multimodal datasets. The fusion of both modalities serves different purposes such as point cloud colorization, geometry enhancement or object detection. However, this fusion task cannot be done directly as both modalities are only coarsely registered. This paper presents a fully automatic approach for LiDAR projection and optical image registration refinement based on LiDAR point cloud 3D renderings. First, a coarse 3D mesh is generated from the LiDAR point cloud using the sensor topology. Then, the mesh is rendered in the image domain. After that, a variational approach is used to align the rendering with the optical image. This method achieves high quality results while performing in very low computational time. Results on real data demonstrate the efficiency of the model for aligning LiDAR projections and optical images

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