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Evaluating the performance of computer vision algorithms is classically done by reporting classification error or accuracy, if the problem at hand is the classification of an object in an image, the recognition of an activity in a video or the categorization and labeling of the image or video. If in addition the detection of an item in an image or a video,(More)
We describe the LIRIS human activities dataset, the dataset used for the ICPR 2012 human activities recognition and localization competition. In contrast to previous competitions and existing datasets, the tasks focus on complex human behavior involving several people in the video at the same time, on actions involving several interacting people and on(More)
In this paper, we present a novel approach for supervised codebook learning and optimization for bag-of-words models. This type of models is frequently used in visual recognition tasks like object class recognition or human action recognition. An entity is represented as a histogram of codewords, which are traditionally clustered with unsupervised methods(More)
Object recognition, human pose estimation and scene recognition are applications which are frequently solved through a decomposition into a collection of parts. The resulting local representation has significant advantages, especially in the case of occlusions and when the subject is non-rigid. Detection and recognition require modelling the appearance of(More)
It is commonly agreed that the success of support vector machines (SVMs), is highly dependent on the choice of particular similarity functions referred to as kernels. The latter are usually handcrafted or designed using appropriate optimization schemes. Multiple kernel learning (MKL) is one possible scheme that designs kernels as sparse or convex linear(More)
Object recognition or human pose estimation methods often resort to a decomposition into a collection of parts. This local representation has significant advantages, especially in case of occlusions and when the " object " is non-rigid. Detection and recognition requires modelling the appearance of the different object parts as well as their spatial layout.(More)
Deep multiple kernel learning is a powerful technique that selects and deeply combines multiple elementary kernels in order to provide the best performance on a given classification task. This technique, particularly effective, becomes intractable when handling large scale datasets; indeed, multiple nonlinear kernel combinations are time and memory(More)
Semi-supervised learning seeks to build accurate classification machines by taking advantage of both labeled and unlabeled data. This learning scheme is useful especially when labeled data are scarce while unlabeled ones are abundant. Among the existing semi-supervised learning algorithms, Laplacian support vector machines (SVMs) are known to be(More)
Multiple kernel learning (MKL) is a widely used technique for kernel design. Its principle consists in learning, for a given support vector classifier, the most suitable convex (or sparse) linear combination of standard elementary kernels. However, these combinations are shallow and often powerless to capture the actual similarity between highly semantic(More)
We consider the problem of learning graphs in a sparse multiclass support vector machines framework. For such a problem, sparse graph penalty is useful to select the significant features and interpret the results. Classical &#x2113;<sub>1</sub>-norm learns a sparse solution without considering the structure between the features. In this paper, a structural(More)