论文标题
关于图像检索的深度散列的调查
A survey on deep hashing for image retrieval
论文作者
论文摘要
Hashhing已被广泛用于大约最近的搜索大规模数据库检索以进行计算和存储效率。 Deep Hashing设计了卷积神经网络体系结构来利用和提取图像的语义信息或特征，最近受到了越来越多的关注。在这项调查中，评估了几种用于图像检索的深层监督哈希方法，我总结了三个主要的不同方向，以实现深度监督的哈希方法。最后发表了一些评论。此外，要突破现有的哈希方法的瓶颈，我提出了一种试试阴影复发的哈希（SRH）方法。具体来说，我设计了一个CNN体系结构来提取图像的语义特征并设计损失功能，以鼓励预测的类似图像。为此，我提出了一个概念：CNN输出的阴影。在优化过程中，CNN输出及其阴影相互引导，以便尽可能获得最佳解决方案。数据集CIFAR-10上的几个实验显示了SRH的令人满意的性能。
Hashing has been widely used in approximate nearest search for large-scale database retrieval for its computation and storage efficiency. Deep hashing, which devises convolutional neural network architecture to exploit and extract the semantic information or feature of images, has received increasing attention recently. In this survey, several deep supervised hashing methods for image retrieval are evaluated and I conclude three main different directions for deep supervised hashing methods. Several comments are made at the end. Moreover, to break through the bottleneck of the existing hashing methods, I propose a Shadow Recurrent Hashing(SRH) method as a try. Specifically, I devise a CNN architecture to extract the semantic features of images and design a loss function to encourage similar images projected close. To this end, I propose a concept: shadow of the CNN output. During optimization process, the CNN output and its shadow are guiding each other so as to achieve the optimal solution as much as possible. Several experiments on dataset CIFAR-10 show the satisfying performance of SRH.
