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We propose an unsupervised approach to segment color images and annotate its regions. The annotation process uses a multi-modal thesaurus that is built from a large collection of training images by learning associations between low-level visual features and keywords. Association rules are learned through fuzzy
regions and words. The third and fourth approaches are based on segmenting the images into homogeneous regions. Both of these approaches rely on a clustering algorithm to learn the association between visual features and keywords. The clustering task is not trivial as it involves clustering a very high-dimensional and sparse
We propose an unsupervised approach to segment color images and annotate its regions. The annotation process uses a multi-modal thesaurus that is built from a large collection of training images by learning associations between low-level visual features and keywords. We assume that a collection of images is available
We propose an unsupervised approach to learn associations between low-level visual features and keywords. We assume that a collection of images is available and that each image is globally annotated. The objective is to extract representative visual profiles that correspond to frequent homogeneous regions, and to
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