Automatic Image Annotation Using Group Sparsity

Automatically assigning relevant text keywords to images is an important problem. Many algorithms have been pro-posed in the past decade and achieved good performance. Efforts have focused upon model representations of key-words, but properties of features have not been well inves-tigated. In most cases, a group of features is preselected, yet important feature properties are not well used to select features. In this paper, we introduce a regularization based feature selection algorithm to leverage both the sparsity and clustering properties of features, and incorporate it into the image annotation task. A novel approach is also proposed to iteratively obtain similar and dissimilar pairs from both the keyword similarity and the relevance feedback. Thus keyword similarity is modeled in the annotation framework. Numerous experiments are designed to compare the perfor-mance between features, feature combinations and regular-ization based feature selection methods applied on the im-age annotation task, which gives insight into the properties of features in the image annotation task. The experimental results demonstrate that the group sparsity based method is more accurate and stable than others. 1