论文标题
旋转不变的深CBIR
Rotation Invariant Deep CBIR
论文作者
论文摘要
卷积神经网络的介绍几乎在每个基于图像的问题上都改善了结果，并且基于内容的图像检索并不例外。但是，CNN功能（旋转不变）会引起构建旋转不变的CBIR系统的问题。尽管可以手工设计旋转不变的功能，但检索精度非常低，因为通过手工工程只能创建低级功能，与创建高级功能以及低级功能的深度学习模型不同。本文通过引入深度学习方向检测模型以及CBIR特征提取模型来构建一种新的方法来构建旋转不变的CBIR系统。本文还强调，这种旋转不变的深CBIR可以实时从大型数据集检索图像。
Introduction of Convolutional Neural Networks has improved results on almost every image-based problem and Content-Based Image Retrieval is not an exception. But the CNN features, being rotation invariant, creates problems to build a rotation-invariant CBIR system. Though rotation-invariant features can be hand-engineered, the retrieval accuracy is very low because by hand engineering only low-level features can be created, unlike deep learning models that create high-level features along with low-level features. This paper shows a novel method to build a rotational invariant CBIR system by introducing a deep learning orientation angle detection model along with the CBIR feature extraction model. This paper also highlights that this rotation invariant deep CBIR can retrieve images from a large dataset in real-time.
