共有结构假设下流形正则图的零样本分类方法

Zero-Shot Classification with Manifold Regularization Graph Based on Common Structure Assumption

零样本学习(Zero-Shot Learning,ZSL)利用视觉和语义特征关联模型进行可鉴别知识迁移,但视觉和语义数据不是简单的对应关系,难以直接建立映射函数。提出一种局部敏感双字典方法,主要贡献有两点:(1)双字典方法。视觉-语义的单字典映射缺乏直接关联的共有变量,提出双字典方法为视觉和语义添加一个共有结构的描述字典,从而构造更合理的视觉-语义关联通道。(2)局部敏感的流形保持方法。在双字典学习中,局部结构信息的描述是关键点,通过构造流形结构图来定义局部敏感约束项,对字典学习和局部流形保持进行联合优化。在AwA和CUB数据集上的实验结果表明,该方法在分类准确率上优于对比算法。

Zero-Shot Learning(ZSL)has utilized association model of visual and semantic features to transfer discriminative knowledge. But visual and semantic data is not a simple correspondence, it’s difficult to directly establish the mapping. A locality sensitive double dictionary method is proposed, which has two main contributions:(1)Double dictionary method.Visual-semantic single dictionary mapping lacks direct associated common variables, a double dictionary method is proposed to add a descriptive dictionary of common structure for visual and semantic features, a more reasonable visualsemantic association channel is constructed.(2)Locality sensitive manifold preserving method. In the double dictionary learning, the description of local structure information is vital. A manifold structure graph is constructed to define locality sensitive regularization, and to jointly optimize the dictionary learning and local manifold preserving. The experimental results on Aw A and CUB datasets show the proposed method outperforms the compared algorithms in accuracy.