Real web community based automatic image annotation

Automatic image annotation is a challenging problem in pattern recognition. Large annotation databases are difficult to build, and the existing models can only label small scale of image sets. In order to solve the problems with the annotation of large databases, a web community based image annotation model is proposed. Firstly, an algorithm for effectively selecting training image is proposed to delete noise images from training dataset. Secondly, a method based on weighted KNN is proposed to assign weights on each image in nearest neighbor collections, which taking into account the impacts of different images in nearest neighbor collections. By this way, training images that are more similar to unlabeled image will have higher confidences in the annotation propagation process. Thirdly, an annotation refinement method based on WordNet level is proposed to improve the annotation results of non-abstract words. Our model is appropriate for annotating images from large-scale real web community. Experiments conducted on large-scale datasets verify the effectiveness of the proposed model.

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► An algorithm is proposed to delete noise images and tags from training dataset.
► A method is proposed to assign weights to each image in nearest neighbor collections.
► A method is proposed to improve the annotation results of non-abstract words.
► The combinations of above methods are used in large scale automatic image annotation.
► Our model is appropriate for annotating images from large-scale real web community.