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In this paper, we present an approach to learning latent semantic analysismodels from loosely annotated images for automatic image annotation andindexing. The given annotation in training images is loose due to: (1)ambiguous correspondences between visual features and annotated keywords; (2)incomplete lists of annotated keywords. The second reason motivates us toenrich the incomplete annotation in a simple way before learning topic models.In particular, some imagined keywords are poured into the incomplete annotationthrough measuring similarity between keywords. Then, both given and imaginedannotations are used to learning probabilistic topic models for automaticallyannotating new images. We conduct experiments on a typical Corel dataset ofimages and loose annotations, and compare the proposed method withstate-of-the-art discrete annotation methods (using a set of discrete blobs torepresent an image). The proposed method improves word-driven probabilityLatent Semantic Analysis (PLSA-words) up to a comparable performance with thebest discrete annotation method, while a merit of PLSA-words is still kept,i.e., a wider semantic range.

Modeling Loosely Annotated Images with Imagined Annotations