document: Learning to tag Social tagging provides valuable and crucial information for large-scale web image retrieval . It is ontology-free and easy to obtain ; however , irrelevant tags frequently appear , and users typically will not tag all semantic objects in the image , which is also called semantic loss . To avoid noises and compensate for the semantic loss , tag recommendation is proposed in literature . However , current recommendation simply ranks the related tags based on the single modality of tag co-occurrence on the whole dataset , which ignores other modalities , such as visual correlation . This paper proposes a multi-modality recommendation based on both tag and visual correlation , and formulates the tag recommendation as a learning problem . Each modality is used to generate a ranking feature , and Rankboost algorithm is applied to learn an optimal combination of these ranking features from different modalities . Experiments on Flickr data demonstrate the effectiveness of this learning-based multi-modality recommendation strategy .
doc_bio_tags: B I I B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O