Zhang Shaoting, Huang Junzhou, Li Hongsheng, Metaxas Dimitris N
Department of Computer Science, Rutgers University, Piscataway, NJ 08854-8019, USA.
IEEE Trans Syst Man Cybern B Cybern. 2012 Jun;42(3):838-49. doi: 10.1109/TSMCB.2011.2179533. Epub 2012 Jan 10.
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, whereas 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. Using this group-sparsity-based method, the whole group of features [e.g., red green blue (RGB) or hue, saturation, and value (HSV)] is either selected or removed. Thus, we do not need to extract this group of features when new data comes. 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. We also show that our framework can be employed in image retrieval tasks by selecting different image pairs. Extensive experiments are designed to compare the performance between features, feature combinations, and regularization-based feature selection methods applied on the image 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.
自动为图像分配相关文本关键词是一个重要问题。在过去十年中已经提出了许多算法并取得了良好性能。以往的工作主要集中在关键词的模型表示上，而特征的属性尚未得到充分研究。在大多数情况下，会预先选择一组特征，但重要的特征属性并未被很好地用于特征选择。在本文中，我们引入一种基于正则化的特征选择算法，以利用特征的稀疏性和聚类属性，并将其纳入图像标注任务。使用这种基于组稀疏性的方法，整个特征组（例如红绿蓝（RGB）或色调、饱和度和明度（HSV））要么被选中，要么被移除。因此，当新数据到来时，我们无需提取这组特征。还提出了一种新颖的方法，从关键词相似度和相关反馈中迭代地获取相似和不相似的图像对。这样，关键词相似度就在标注框架中得到了建模。我们还表明，通过选择不同的图像对，我们的框架可用于图像检索任务。我们设计了大量实验，以比较在图像标注任务中应用的特征、特征组合以及基于正则化的特征选择方法之间的性能，这有助于深入了解图像标注任务中特征的属性。实验结果表明，基于组稀疏性的方法比其他方法更准确、更稳定。
