论文标题
HDF：场景图像表示的混合深度功能
HDF: Hybrid Deep Features for Scene Image Representation
论文作者
论文摘要
如今，将从预训练的深度学习模型中提取的功能作为图像表示，已经实现了有希望的分类性能。现有方法通常仅考虑基于对象的功能或基于场景的功能。但是，两种功能对于场景图像等复杂图像都很重要，因为它们可以相互补充。在本文中，我们提出了一种新型的功能 - 混合深度特征，用于场景图像。具体而言，我们在两个级别上利用基于对象的和基于场景的特征：部分图像级别（即图像的一部分）和整个图像级别（即整个图像），这将产生四种类型的深度特征的总数。关于零件图像级别，我们还提出了两种新的切片技术来提取基于零件的特征。最后，我们通过串联操作员汇总了这四种类型的深度特征。我们在场景图像分类任务方面，在三个常用的场景数据集（MIT-67，Scene-15和Event-8）上展示了混合深度功能的有效性。广泛的比较表明，我们引入的功能可以产生最新的分类精度，这些精度比所有数据集中现有功能的结果更一致和稳定。
Nowadays it is prevalent to take features extracted from pre-trained deep learning models as image representations which have achieved promising classification performance. Existing methods usually consider either object-based features or scene-based features only. However, both types of features are important for complex images like scene images, as they can complement each other. In this paper, we propose a novel type of features -- hybrid deep features, for scene images. Specifically, we exploit both object-based and scene-based features at two levels: part image level (i.e., parts of an image) and whole image level (i.e., a whole image), which produces a total number of four types of deep features. Regarding the part image level, we also propose two new slicing techniques to extract part based features. Finally, we aggregate these four types of deep features via the concatenation operator. We demonstrate the effectiveness of our hybrid deep features on three commonly used scene datasets (MIT-67, Scene-15, and Event-8), in terms of the scene image classification task. Extensive comparisons show that our introduced features can produce state-of-the-art classification accuracies which are more consistent and stable than the results of existing features across all datasets.
