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To understand which concepts are visualizable and to what extent they can be visualized are worthwhile for multimedia and computer vision research. Unfortunately, few previous works have ever touched such topics. In this paper, we propose an unified model to automatically identify visual concepts and estimate their visual characteristics, or visualness, from a large-scale image dataset. To this end,...
Although it has been extensively studied for many years, automatic image annotation is still a challenging problem. Recently, data-driven approaches have demonstrated their great success to image auto-annotation. Such approaches leverage abundant partially annotated web images to annotate an uncaptioned image. Specifically, they first retrieve a group of visually closely similar images given an uncaptioned...
Easy photo-taking and photo-sharing today make image an increasingly important type of media in people's everyday life, which arouses a growing demand for a practical image understanding technique. Traditional computer vision or machine learning methods which learn models based on a set of training data are still in the stage of tackling hundreds of object categories. Such a scale is far from practical...
Though it has cost great research efforts for decades, object recognition is still a challenging problem. Traditional methods based on machine learning or computer vision are still in the stage of tackling hundreds of object categories. In recent years, non-parametric approaches have demonstrated great success, which understand the content of an image by propagating labels of its similar images in...
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