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ability to include human perception in the query for retrieval because the query must be an image. In this paper a new image annotation technique and a keyword-based image retrieval system are presented, which map the low-level features of the Joint Composite Descriptor to the high-level features constituted by a set of
In this paper, we describe the use of a Boosting algorithm, Real AdaBoost, for content-based image retrieval (CBIR) on a large number (190) of keyword categories. Previous work with Boosting for image orientation detection has involved only a few categories, such as a simple outdoor vs. indoor scene dichotomy. Other
Automatic image annotation is the process of assigning keywords to digital images depending on the content information. In one sense, it is a mapping from the visual content information to the semantic context information. In this study, we propose a novel approach for automatic image annotation problem, where the
Recently, with the increasing of users and activities in social network service, an image sentiment analysis has been an important keyword for psychological study and commercial marketing. To recognize accurately user's sentiments of the image, it is essential to identify discriminative visual features and then
Finding information based on an object's profile is very useful when exact keywords for the object are unknown. Current image retrieval system all ignores the color information, for example we want to find a super-star with a piece of red petticoat, or we want to a red flower with white background. They all cannot
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