一种改进的二进制哈希编码方法

为了应对手工视觉特征与哈希编码过程不能最佳地兼容以及现有哈希方法无法区分图像语义信息的问题，提出一种基于深度卷积神经网络学习二进制哈希编码的方法.该方法基本思想是在深度残差网络中增加一个哈希层，同时学习图像特征和哈希函数；以此同时提出一种更加紧凑的分级哈希结构，用来提取更加接近图像语义的特征.经MNIST、CIFAR-10、NUS-WIDE数据集的实验，结果表明该方法优于现有的哈希方法.该方法不仅统一了特征学习和哈希编码的过程，同时深层残差网络也能得到更接近图像语义的特征，进而提高了检索准确度.

An Improved Binary Hash Coding Method

To address the problems that hand-engineering visual features can't be optimally compatible with the Hash coding process and existing Hash methods can't differentiate images semantics information,a learning method of binary Hashing based on deep convolutional neural networks is proposed.The basic idea is to add a Hash layer into the deep residual network and to learn simultaneously image features and Hash functions.Meanwhile,we propose a more compact hierarchical Hashing structure to extract features closer to semantics information of images.Experimental results of MNIST,CIFAR-10 and NUS-WIDE datasets show that the method is superior to existing Hashing methods.This method not only unifies the process of feature learning and Hash coding,and at the same time,the deep residual network is able to get features closer to image semantics.Thus the retrieval accuracy is improved.