Automatic Image Annotation Using Group Sparsity

Automatically assigning relevant text keywords to images is an important problem. Many algorithms have been proposed in the past decade and achieved good performance. Efforts have focused upon model representations of keywords, but properties of features have not been well investigated. 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 performance between features, feature combinations and regularization based feature selection methods applied on the image annotati...

• Type: Conference
• Year: 2010
• Where: CVPR
• Authors: Shaoting Zhang, Junzhou Huang, Yuchi Huang, Yang Yu, Metaxas Dimitris
• Keywords: Computer Vision, CVPR 2010, Image Annotation Task, Important Feature Properties, Regularization Based Feature