In most image retrieval systems, images include various high-level semantics, called tags or annotations. Virtually all the state-of-the-art image annotation methods that handle imbalanced labeling are search-based techniques which are time-consuming. In this paper, a novel coupled dictionary learning approach is proposed to learn a limited number of visual prototypes and their corresponding semantics simultaneously. This approach leads to a real-time image annotation procedure. Another contribution of this paper is that utilizes a marginalized loss function instead of the squared loss function that is inappropriate for image annotation with imbalanced labels. We have employed a marginalized loss function in our method to leverage a simple and effective method of prototype updating. Meanwhile, we have introduced \({\ell }_1\) regularization on semantic prototypes to preserve the sparse and imbalanced nature of labels in learned semantic prototypes. Finally, comprehensive experimental results on various datasets demonstrate the efficiency of the proposed method for image annotation tasks in terms of accuracy and time. The reference implementation is publicly available at https://github.com/hamid-amiri/MCDL-Image-Annotation.

在大多数图像检索系统中，图像包括各种高级语义，称为标签或注释。几乎所有处理不平衡标签的最先进的图像注释方法都是基于搜索的技术，这些技术非常耗时。在本文中，提出了一种新颖的耦合字典学习方法来同时学习有限数量的视觉原型及其相应的语义。这种方法导致实时图像注释过程。本文的另一个贡献是利用边缘化损失函数而不是平方损失函数，这不适用于带有不平衡标签的图像注释。我们在我们的方法中采用了边缘化损失函数来利用简单有效的原型更新方法。同时，我们推出了\({\ell }_1\)对语义原型进行正则化，以保持学习语义原型中标签的稀疏和不平衡性质。最后，在各种数据集上的综合实验结果证明了所提出的图像标注任务方法在准确性和时间方面的有效性。参考实现在 https://github.com/hamid-amiri/MCDL-Image-Annotation 上公开提供。