A stochastic grammar of images
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A Hierarchical and Contextual Model for Aerial Image Parsing
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From region based image representation to object discovery and recognition
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ClassCut for unsupervised class segmentation
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Skeleton Search: Category-Specific Object Recognition and Segmentation Using a Skeletal Shape Model
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Accurate Object Recognition with Shape Masks
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Incorporating shape into spatially-aware adaptive object segmentation algorithm
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Weakly Supervised Localization and Learning with Generic Knowledge
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Bottom-up perceptual organization of images into object part hypotheses
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Drawing an automatic sketch of deformable objects using only a few images
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Learning-based object segmentation using regional spatial templates and visual features
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Classification improvement of local feature vectors over the KNN algorithm
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Learning discriminative localization from weakly labeled data
Pattern Recognition
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Suppose a set of images contains frequent occurrences of objects from an unknown category. This paper is aimed at simultaneously solving the following related problems: (1) unsupervised identification of photometric, geometric, and topological (mutual containment) properties of multiscale regions defining objects in the category; (2) learning a region-based structural model of the category in terms of these properties from a set of training images; and (3) segmentation and recognition of objects from the category in new images. To this end, each image is represented by a tree that captures a multiscale image segmentation. The trees are matched to find the maximally matching subtrees across the set, the existence of which is itself viewed as evidence that a category is indeed present. The matched subtrees are fused into a canonical tree, which represents the learned model of the category. Recognition of objects in a new image and image segmentation delineating all object parts are achieved simultaneously by finding matches of the model with subtrees of the new image. Experimental comparison with state-of-the-art methods shows that the proposed approach has similar recognition and superior localization performance while it uses fewer training examples.