Sparse Representation for Multi-Label Image Annotation

Image annotation is the process of assigning proper keywords to describe the content of a given image, which can be regarded as a problem of multi-object image classification. In this paper, a general multi-label annotation algorithm is proposed, which is based on sparse representation theory and employs a multi-level decision method to deal with the multi-object classification problem. The experimental results show that the proposed algorithm can provide more promising results compared with the traditional classification based image annotation methods.