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Classic <i>Content-Based Image Retrieval</i> (CBIR) takes a single non-annotated query image, and retrieves similar images from an image repository. Such a search must rely upon a holistic (or global) view of the image. Yet often the desired content of an image is not holistic, but is localized. Specifically, we define <i>Localized Content-Based Image(More)
Salient points are locations in an image where there is a significant variation with respect to a chosen image feature. Since the set of salient points in an image capture important local characteristics of that image, they can form the basis of a good image representation for content-based image retrieval (CBIR). The features for a salient point should(More)
We present MI-Winnow, a new multiple-instance learning (MIL) algorithm that provides a new technique to convert MIL data into standard supervised data. In MIL each example is a collection (or bag) of d-dimensional points where each dimension corresponds to a feature. A label is provided for the bag, but not for the individual points within the bag.(More)
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