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Hybrid Relative Attributes Based on Sparse Coding for Zero-Shot Image Classification

机译：基于稀疏编码的混合相对属性进行零拍图像分类

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摘要

As a specific case of image recognition, zero-shot image classification is difficult to solve since its training set cannot cover all the categories of the testing set. From the view point of human vision recognition, the objects can be recognized through the visible and nameable description to the properties. To be the semantic description of the object property, attributes can be taken as a bridge between the seen and unseen categories, which are capable of using into zero-shot image classification. There are mainly binary attributes and relative attributes for zero-shot classification, where the relative attributes have the ability to catch more general sematic relationship than the binary ones. But relative attributes do not always work in zero-shot classification for those categories having similar relative strength attributes. Aiming at solving the defect of the relative attributes in describing the similar categories, we propose to construct the Hybrid Relative Attributes based on Sparse Coding (SC-HRA). First, sparse coding is implemented on low-level features to get nonsemantic relative attributes, which are the necessary complement to the existing relative attributes. After that, they are integrated with the relative attributes to form the hybrid relative attributes (HRA). HRA ranking functions are then learned by the relative attribute learning. Finally, the class label is obtained according to the predicted ranking results of HRA and the ranking relations of HRA among the categories. To verify the effectiveness of SC-HRA, the extensive experiments are conducted on the datasets of faces and natural scenes. The results show that SC-HRA acquires the higher classification accuracy and AUC value.
机译：作为图像识别的具体情况，由于其训练集无法涵盖测试集的所有类别，因此难以解决零拍图像分类。从人体视觉识别的视点来看，可以通过可见和可行的描述来识别对象。据对象属性的语义描述，可以将属性作为所看到的和看不见的类别之间的桥梁，能够使用零拍摄图像分类。零拍摄分类主要有二进制属性和相对属性，其中相对属性具有比二进制文件更普遍的语义关系。但相对属性并不总是为具有相似强度属性的类别的类别中的零拍分类工作。旨在解决在描述类似类别时的相对属性的缺陷，我们建议基于稀疏编码（SC-HRA）构建混合相对属性。首先，在低级功能上实现稀疏编码，以获取非对象属性，这是对现有相对属性的必要补充。之后，它们与相对属性集成以形成混合相对属性（HRA）。然后通过相对属性学习学习HRA排名函数。最后，根据HRA的预测排名结果和类别中的HRA的排名关系获得了类标签。为了验证SC-HRA的有效性，在面部和自然场景的数据集上进行了广泛的实验。结果表明，SC-HRA获取较高的分类精度和AUC值。

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  • 来源
    《Mathematical Problems in Engineering》 |2019年第5期|7390327.1-7390327.13|共13页
  • 作者

    Lu Nannan; Sun Yanjing; Yun Xiao;

  • 作者单位

    China Univ Min & Technol Sch Informat & Control Engn Xuzhou Jiangsu Peoples R China;

    China Univ Min & Technol Sch Informat & Control Engn Xuzhou Jiangsu Peoples R China;

    China Univ Min & Technol Sch Informat & Control Engn Xuzhou Jiangsu Peoples R China;

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