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              <p>In this article, we consider the task of category level object segmentation. Object models based on bag-of-words representations achieve state-of-the-art performance for object recognition. However, they fail to accurately locate object boundaries and thus produce inaccurate object segmentation. On the other hand, Markov Random Field based models used for image segmentation focus on object boundaries but can hardly use global object constraints, which is required when dealing with object categories whose appearance may vary significantly. The key contribution of this paper is to combine the advantages of these two approaches. First, a blob-based mechanism allows to detect objects using visual word occurrences, and produces rough image segmentation. Second, a MRF component produces clean cuts, guided by local image cues (color, texture and edge cues) and by long-distance dependency given by the blob model, which enforces label consistency. Our approach is validated on standard public datasets, containing different object classes, in presence of cluttered backgrounds and large view point changes.</p>
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              <p>Nous nous intéressons ici à la segmentation d'images, et plus particulièrement à la segmentation de catégories d'objets. Si les modèles d'apparence par sac-demots donnent à ce jour les meilleures performances en termes de reconnaissance d'objets, ils ne permettent pas de segmenter précisément les frontières des objets. Parallèlement, les modèles utilisant des champs de Markov (MRF) utilisés pour la segmentation d'images se basent essentiellement sur les frontières et permettent une régularisation spatiale, mais utilisent difficilement des contraintes globales liées aux objets, ce qui est indispensable lorsqu'on travaille avec des catégories d'objets dont l'apparence peut varier significativement d'une instance à l'autre. La principale contribution de cet article est la combinaison de ces deux approches. Notre approche comporte un mécanisme de détection d'objets par sac-de-mots qui produit une segmentation grossière des images, et simultanément, un second mécanisme, utilisant un MRF, produit des segmentations précises. Notre approche est validée sur plusieurs bases publiques de référence, contenant différentes classes d'objets en présence de fonds encombrés et présentant de larges changements de points de vue.</p>
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