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 on this https URL

本文提出了一种结合字典学习和边缘损失函数的方法，实现了实时图像注释，并在准确性和时间方面展示了其高效性。

基于边缘耦合字典学习的实时图像标注

Toward Real-Time Image Annotation Using Marginalized Coupled Dictionary  Learning

Zero-shot learning (ZSL) aims to recognize objects of novel classes without
any training samples of specific classes, which is achieved by exploiting the
semantic information and auxiliary datasets. Recently most ZSL approaches focus
on learning visual-semantic embeddings to transfer knowledge from the auxiliary
datasets to the novel classes. However, few works study whether the semantic
information is discriminative or not for the recognition task. To tackle such
problem, we propose a coupled dictionary learning approach to align the
visual-semantic structures using the class prototypes, where the discriminative
information lying in the visual space is utilized to improve the less
discriminative semantic space. Then, zero-shot recognition can be performed in
different spaces by the simple nearest neighbor approach using the learned
class prototypes. Extensive experiments on four benchmark datasets show the
effectiveness of the proposed approach.

本文提出了一种基于耦合字典学习的零样本学习方法，该方法通过利用种类原型中的区分性信息来对齐视觉 - 语义结构，进而提高不太具有区分性的语义空间的表现，并通过简单的最近邻方法在不同空间上执行零样本识别。在四个基准数据集上的实验证明了该方法的有效性。